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Tropospheric Greenhouse Gases: Emission

Characteristics and Dynamical Effects

A thesis submitted in partial fulfillment of

the requirements for the degree of

Doctor of Philosophy

by

Naveen Chandra

(Roll No. 11330007)

Under the guidance of

Prof. Shyam Lal

Space and Atmospheric Sciences Division

Physical Research Laboratory, Ahmedabad, India.

DISCIPLINE OF PHYSICS

INDIAN INSTITUTE OF TECHNOLOGY GANDHINAGAR

2015

to

my parents and grand parents

Declaration

I declare that this written submission represents my ideas in my own words

and where others’ ideas or words have been included, I have adequately cited and

referenced the original sources. I also declare that I have adhered to all principles

of academic honesty and integrity and have not misrepresented or fabricated or

falsified any idea/data/fact/source in my submission. I understand that any

violation of the above will be cause for disciplinary action by the Institute and

can also evoke penal action from the sources which have thus not been properly

cited or from whom proper permission has not been taken when needed.

(Signature)

(Name: Naveen Chandra)

(Roll No: 11330007)

Date: December 30, 2015

CERTIFICATE

It is certified that the work contained in this thesis titled “Tropospheric

Greenhouse Gases: Emission Characteristics and Dynamical Effects”

by Mr. Naveen Chandra (Roll No. 11330007), has been carried out under my

supervision and that this work has not been submitted elsewhere for a degree.

Prof. Shyam Lal

(Thesis Supervisor)

Space and Atmospheric Sciences Division

Physical Research Laboratory,

Ahmedabad, India.

Date: December 30, 2015

i

Acknowledgements

Ph.D diary, witness of five and half wonderful years of my journey, has reached

in its closing phase with the pages of bittersweet experiences and nostalgic memo-

ries. A happy ending note is that, in spite of being alone on the first page of this

journey, now I find myself in the company of many wonderful people on the last

page of this journey. Some of them I admire and respect and some of them became

my friends. It is not possible to list here all the people who influenced my life and

worked to achieved this milestone. However, even if they are not mentioned here,

I am grateful to all of them nonetheless.

I feel esteemed pleasure and privilege to express my profound sense of gratitude

and thankfulness to my mentor, philosopher and Ph.D supervisor Prof. Shyam

Lal for his invaluable and meticulous guidance, constant supervision, and con-

tinuous encouragement on every advancement that I have made in this journey.

In addition, his recognition of my weaknesses and pushing me to overcome them

has been the most gratifying part of this experience. Not only academically, but

personally also he has always been very supportive and helpful in all aspects dur-

ing my entire stay in Ahmedabad and I will forever remember his kindness and

support.

I am grateful to Prof. Varun Sheel and Prof. Sunil Singh for their continuous

support, encouragement, constructive feedbacks, suggestions from time to time

and critical evaluation of this thesis before its submission. I would also like to

thank Prof. S. Ramachandran for his helping hand and support throughout the

tenure of this work. I am deeply indebted to Mr. S. Venkataramani for his valu-

able help in learning the fundamentals of measurements, his wholehearted support

and many useful interactions. I am also grateful to Mr. T. A. Rajesh for his

unselfish support, helpful discussions in science and encouragement at every step

of my scientific journey. I gratefully acknowledge Mr. T. K. Sunil Kumar for

his continuous support in the operation and maintenance of various instruments.

I am highly obliged to Dr. M. Naja (ARIES, Nainital), Dr. S. K. Sharma and

ii

Dr. L. K. Sahu for various scientific discussions and motivations. I also cherish

my interactions with Dr. Y. B. Acharya, Prof. R. Sekar, Prof. D. Pallamraju,

Prof. H. Chandra, Prof. S.A. Haider, Dr. D. Chakrabarty, Prof J. Bhatt, Prof.

S. Mohanty, Dr. Navin Juyal, Dr. Namit Mahajan, Dr. R. D. Deshpande, Prof.

R. Sridharan, Dr. A. Guharay, Prof. H. S. Sinha, Mr. R.P. Singh, Mr. Atul

Manke and Mr. Mitesh Bhavsar. I will be forever indebted to Prof. R. Ramesh

and Prof. R. Rangarajan for their inspiring lectures during our course work.

I would like to express my sincere thanks to the Director and the Dean of

PRL, for providing me the necessary facilities, academic, non-academic support

and help to complete my research work. The moments, which I spent in PRL

during my research tenure, will always remind me the golden moments of my life.

I thank all the PRL “academic committee members” during 2010 − 2015, whose

critical review and guidance at various stages of my thesis tenure helped me to im-

prove in my research pursuits. I would like to thank Dr. Bhushit Vaishnav, Head

Academic Services, PRL, for his support during this period. My warm thanks

extend to Director, Dean and all staffs member of IIT Gandhinagar, specially

Piyush Bhai for their kind help and co-operation.

During this work I have collaborated with many colleagues for whom I have

great regard, and I wish to extend my warmest gratefulness to all of them. I owe

my most sincere gratitude to Dr. Prabir Patra from JAMSTEC, Japan and con-

sider myself lucky to get an opportunity to work with him. I have been greatly

benefited and learned very much from his exemplary scientific and personal in-

tegrity, generosity, never ending enthusiasm, and positive approach to understand

research problems. He always showed deep interest in my work and provided time

from his busy schedule for many useful and insightful discussions. I extend my

sincere thanks to to Prof. Michel Ramonet (LSCE, France) for providing me an

opportunity to visit his laboratory LSCE, Paris, for scientific discussions as well

as visiting their atmospheric measurement lab. I am truly thankful to Xin Lin for

her wholehearted support and various fruitful discussions regarding the methane

iii

modeling during my stay in Paris.

I have been blessed with intellectual guidance, inspiration and moral support

from my school teachers, specially from Shri O.P. Semwal. I am extremely grate-

ful to all of them. I wish to express my warm and sincere thanks to Dr. Ishan

Purohit, who persuaded me for this journey as well as gave me moral support dur-

ing my struggling days. The critical supports of CONTRAIL, MOPITT, TRMM

and NCEP science team members for providing the in-situ and satellite data prod-

ucts, are greatly acknowledged.

Many PRL staff members have also been extremely helpful in advancing my

research. I thank all the staff members of PRL computer center especially to Jigar

ji, Hitendra ji, Alok ji, Samual ji and Tejas ji, for providing excellent computa-

tional and Internet facilities. I express my appreciation for the cooperation and

help extended by the library and its staff members in general and Mrs. Nistha

Anilkumar and Pragya Pandey in particular. In addition, I would like to express

my deepest appreciation to workshop people (Ubale ji and his team members; Vipul

bhai, Vishnu bhai, Rajesh Kaila and others), administrative and technical staff

(Hemal ji, Gandhi ji, Parul ben, Preeti ben, Nandini ben, Richa ben, Jaishree

ben Yadav ji, Gupta ji, Senthil ji, Sashi Ji, Shivadasan ji, Hota ji, Ranganathan

ji, Pradeep ji, Bankim bhai and Sneha Nair), for their helpful hands and noble

cooperation throughout the tenure of my thesis.

Completing a thesis is an intensive learning process on many different levels

and one of the most important lessons I have learned during this work is the value

of support from the friends, I deeply respect their encouragement, love and care.

I would remain ever grateful to my lab seniors Chinmay Mallik and Shuchita

Srivastava for their selfless help, consistent encouragement and interest in the

progress of my work. The most amazing officemates Ashim and Jagat are espe-

cially acknowledged, since without their corporation and joyful company, it was

not be so easy to accomplish this work. I feel blessed to have seniors like Arvind,

iv

Pankaj, Vineet and Vimal, with whom I have interacted and spent maximum

time during my beginning PRL days and due to have such seniors this memorable

journey started very smoothly. The company and innumerable late night gather-

ings with Girish Kumar, Arko, Monojit, Tanmoy (Chattopadhyay), and Chandan

(Hati) have kept me sane and focused on the other fun things in life, I sincerely

thank them for adding such pleasant memories in my life. I feel fortunate to have

wonderful company of my batchmates Avdhesh, Aadhi, Anjali, Bhavya, Damu,

Dillip, Dinesh, Gaurav (Tomar), Gaurav (Sharma), Gangi, Girish, Gulab, Lek-

shmy, Midhun, Priyanka, Upendra, Uma, Wageesh and Yashpal, who fills differ-

ent colours in my life and provided me huge moral supports during my struggling

days of my research. My dearest junior friends Shraddha, Ikshu, Arun, Manu,

Mondal, Anirban, and Abhaya deserve special mention for their enormous love,

care, support and deep respect for me. In addition, I also express my sincere

thanks to Guru, Kuldeep, Sanjay, Tiwari, Chithra, Ujjal, Sushanta, Sunil, Arun

(Awasthi), Abhishek (Basak), Sashi, Fazlul, Sneha (Yadav), Shweta, Ila, Amren-

dra, Deepti, Deepak, Bivin, Chandana, Upasana, Sneha (Shawant), Lalit, Rahul,

Jinia, Apurv, Venky, Newton, Pankaj, Jabir, Ali, Navpreet, Rukmani, Rupa,

Satish, Kumar, Chandan (Gupta), Devendra, Manish, Wriju, Niharika, Shubha,

Ravi, Debrup, Sudip, Kaustav, Varun, Subir and several others who made my

stay at PRL cozy and made the hostel a happening place. I greatly acknowledge to

Narendra (ARIES, Nainital), Tapaswini (ARIES, Nainital) and Srabanti Ballav

(JU, Kolkata) for giving me a hand on experience on atmospheric model (WRF).

I am also thankful to all those who were around me directly or indirectly dur-

ing this work, specially, Piyush, Santosh, Naresh and my long time friends Alok,

Tinku, Pari, Deepak, Ajay, Gaurav and Himanshu for their constant support and

encouragements. Their constant push and motivational chats have always helped

me to recover from odd times.

Above all, I owe my deepest respect and gratitude to my parents and grand-

parents for their love, encouragement and unending support. Beyond giving me

life itself, my parents made countless sacrifices as they cared for and nurtured

v

me through my infancy and childhood, provided me with the necessities of life,

nursed me through physical illnesses and the emotional stresses of growing up.

They are an unfailing inspiration and source of strength and joy for me. Special

thanks to my younger sister Nutan and younger brother Praveen, who made my

periodic visits to home a delight every time. Last but not the least, I express my

gratefulness towards my entire family members who always encouraged me during

my thesis period. I personally believe that my thesis is purely an outcome of my

parents and grandparents blessings and prayers, and that is why I dedicate this

dissertation to them.

(Naveen Chandra)

vii

Preface

The urban areas consist of more than half of the world’s population and con-

tribute about 70% of the fossil fuel CO2 emissions (in 2010) as well as significant

amount of anthropogenic emissions of CH4 into the atmosphere. India is a fast

developing country, where fossil fuel emissions have increased dramatically in

last three decade and further predicted to continue to grow at least 6% per year

through to 2025. In order to provide independent verification of future mitigation

activities and predicting future climate, there is an urgent need of the measure-

ments of greenhouse gases over representative urban regions.

Realising the importance of the study of greenhouse gases over urban loca-

tions, measurements of CO2 and CH4 along with an anthropogenic emission tracer

CO have been initiated at a major urban site Ahmedabad in India, using a state

of the art laser based cavity ring down spectrometer. The observed year-long

data are analysed using suitable global chemistry transport models (JAMSTECs

ACTM for CO2 and CH4, LMDz−OR−INCA for CH4), and correlations among

them. The effects of land ecosystem on CO2 variation as well as anthropogenic

emissions and atmospheric transport on CO2 and CH4 variations have been iden-

tified. The inventory (EDGAR) emissions of CH4 and CO are tested using the

observed data for the study location and found that the inventory emissions are

underestimated. The CO2 observations and model comparison brings out the

need for improvement in the terrestrial flux simulated by the Carnegie-Anes-

Stanford Approach (CASA) ecosystem model. Furthermore, comparison of the

seasonal cycle of CH4 with both models revel that the afternoon mixing ratios

have the potential to represent the footprint of its emission of larger area and

hence this data can be used in regional and global CH4 inversion study with

some caution.

Analyses of vertical profiles of CO2 and CO in the troposphere using aircraft

measurements of CO2 over Delhi and satellite data of CO over five selected re-

gions in India are also conducted. The amplitude of seasonal variability in CO2

viii

shows direct, but a delayed link with the strength of the Indian summer mon-

soon rainfall in Delhi. The model simulations are more close to the observations

in the upper troposphere (3-8 km) as compared to lower troposphere (below 3

km). Average seasonal distributions of CO in the troposphere over all the study

regions show very large spatial and vertical variability. Significant contribution

at 300 − 200 hPa, due to strong convection during the monsoon season as well

as long range transport from the biomass burning regions of Central Africa and

East Asia during other seasons particularly over south of Ahmedabad, have been

observed. The annual variations in the CO concentrations at 900 and 300 hPa are

found to be in opposite phase with lower values during monsoon at 900 hPa and

higher values at 300 hPa. Simulations from two photochemical-transport models

(MOZART and EMAC) are able to capture these variations broadly.

Measurements of CO2 and CH4 coupled with the atmospheric transport mod-

els could be used to assess the sources and sinks of CO2 and CH4 at regional level

by inverse modeling studies. This is, however an objective for the future, since it

can only be achieved using data records covering multiple years and at a network

of stations. In summary, this thesis contributes in providing precise atmospheric

measurements of important GHGs - CO2 and CH4 along with the anthropogenic

tracer CO over an urban location in India which is not done so far to the best

of our knowledge. This dataset is very helpful for understanding processes and

phenomena related to the land-atmosphere exchange of CO2, constraining the

CH4 and CO emission inventories as well as understanding the contributions of

anthropogenic sources in a mega-city to observed variations.

Keywords: Greenhouse gases, CRDS technique, India, Urban region, An-

thropogenic emissions, Biospheric emissions, Long range transport, Photochemical-

transport models.

ix

Acronyms and Abbreviations

ACTM AGCM-based chemistry- transport model

AGCM Atmospheric general circulation model

AK Averaging Kernel

AMSL Above Mean Sea Level

CARIBIC Civil Aircraft for the regular Investigation of the atmosphere Based on an Instrument Container

CASA Carnegie-Ames-Stanford Approach

CFC Chlorofluorocarbon

CH4 Methane

CO2 Carbon dioxide

CO Carbon monoxide

CME Continuous CO2 Measuring Equipment

CNG Compressed Natural Gas

CRDS Cavity Ring Down Spectroscopy

CRI Cape Rama

CSIRO Commonwealth Scientific and Industrial Research Organization

CTM Chemistry Transport Model

DOF Degrees Of Freedom

ECHAM European Centre Hamburg general circulation model

ECMWF European Centre for Medium-range Weather Forecasting

EDGAR Emissions Database for Global Atmospheric Research

GCM General Circulation Model

GHGs Greenhouse gases

GMT Greenwich Mean Time

GPP Gross Primary production

HLE Hanle

HYSPLIT Hybrid Single Particle Lagrangian Integrated Trajectory Model

IGP Indo Gangetic Plain

INCA Interactions between Chemistry and Aerosols

IPCC Intergovernmental Panel on Climate Change

IR Infrared radiation

ITCZ Inter-Tropical Convergence Zone

JAL Japan Airline

LT Lower troposphere

MB Mean Bias

MEERA Modern-Era Retrospective Analysis for Research and Applications

x

MESSy Modular Earth Submodel System

MLO Mauna Loa

MNMB Modified and Normalized Mean Bias

MODIS Moderate Resolution Imaging Spectroradiometer

MOPITT Measurements Of Pollution In The Troposphere

MOZART Model of Ozone and Related Tracers

MT Middle troposphere

N2O Nitrous oxide

NCEP National Centers for Environmental Prediction

NDIR Non Dispersive Infrared

NIO National Institute of Oceanography

ORCHIDEE Organizing Carbon and Hydrology in Dynamic Ecosystems

PBL Planetary Boundary Layer

POET Precursors of Ozone and their Effects in the Troposphere

POB Port Blair

PON Pondicherry

PPB Parts per Billion

PPM Parts per Million

PRL Physical Research Laboratory (Ahmedabad, India)

PTFE Polytetrafluoroethylene

REAS Regional Emission inventory in Asia

RMSE Root Mean Square Error

SEY Seychelles

SNG Sinhagad

TRMM Tropical Rainfall Measuring Mission

UT Upper troposphere

VC Ventilation Coefficient

WMO World Meteorological Organization

Contents

Acknowledgements i

Abstract vii

Acronyms and Abbreviations ix

List of Figures xv

List of Tables xix

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Greenhouse gases and their role in Earth’s atmosphere . . . . . . 4

1.2.1 Radiation budget . . . . . . . . . . . . . . . . . . . . . . . 6

1.2.2 Greenhouse effect . . . . . . . . . . . . . . . . . . . . . . . 8

1.3 Budget of direct and indirect Greenhouse Gases . . . . . . . . . . 11

1.3.1 CO2 budget . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.3.2 CH4 budget . . . . . . . . . . . . . . . . . . . . . . . . . . 16

1.3.3 CO budget . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4 Tools for studying the effects of GHGs on Earth’s climate . . . . . 21

1.4.1 Radiative Forcing (RF) . . . . . . . . . . . . . . . . . . . . 21

1.4.2 Global Warming Potential (GWP) . . . . . . . . . . . . . 24

1.5 Indian GHGs measurements and need for further study . . . . . . 25

1.6 Outline of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . 27

xi

xii CONTENTS

2 Methodology 31

2.1 In-situ measurement techniques . . . . . . . . . . . . . . . . . . . 32

2.1.1 NDIR technique . . . . . . . . . . . . . . . . . . . . . . . . 33

2.1.2 CRDS technique . . . . . . . . . . . . . . . . . . . . . . . 34

2.2 Effect of water vapor on CO2 measurements . . . . . . . . . . . . 37

2.3 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.3.1 Ground based measurements of CO2, CH4 and CO . . . . 38

2.3.2 Airborne measurements of CO2 . . . . . . . . . . . . . . . 42

2.4 Remote sensing measurements of CO . . . . . . . . . . . . . . . . 42

2.5 Description of the photochemical-transport models . . . . . . . . 45

2.5.1 ACTM model for CO2 . . . . . . . . . . . . . . . . . . . . 46

2.5.2 ACTM model for CH4 . . . . . . . . . . . . . . . . . . . . 46

2.5.3 LMDz-OR-INCA model for CH4 . . . . . . . . . . . . . . . 47

2.5.4 MOZART model for CO . . . . . . . . . . . . . . . . . . . 48

2.5.5 EMAC model for CO . . . . . . . . . . . . . . . . . . . . . 49

2.6 Statistical metrics for checking model performances . . . . . . . . 50

3 Study of CO2 and CO over an urban location 53

3.1 Site description, local emission sources and meteorology . . . . . . 55

3.2 Measurement technique . . . . . . . . . . . . . . . . . . . . . . . . 57

3.3 Time series analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 58

3.4 Seasonal patterns of CO2 and CO . . . . . . . . . . . . . . . . . . 60

3.5 Diel cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

3.5.1 Diel cycle of CO2 . . . . . . . . . . . . . . . . . . . . . . . 64

3.5.2 Diel cycle of CO . . . . . . . . . . . . . . . . . . . . . . . 67

3.6 Correlation between CO and CO2 . . . . . . . . . . . . . . . . . . 70

3.7 Top-down CO emissions from observations . . . . . . . . . . . . . 73

3.8 Diurnal tracking of CO2 emissions . . . . . . . . . . . . . . . . . . 75

3.9 Model - Observations comparison of CO2 . . . . . . . . . . . . . . 78

3.9.1 Diel cycle comparision . . . . . . . . . . . . . . . . . . . . 78

3.9.2 Seasonal cycle comparison . . . . . . . . . . . . . . . . . . 80

3.10 Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

CONTENTS xiii

4 Tropospheric distributions of CO2 and CO 87

4.1 Tropospheric distribution of CO2 over Delhi . . . . . . . . . . . . 89

4.1.1 Site description . . . . . . . . . . . . . . . . . . . . . . . . 89

4.1.2 Vertical distribution of CO2 . . . . . . . . . . . . . . . . . 90

4.1.3 Seasonal variation of CO2 . . . . . . . . . . . . . . . . . . 94

4.1.4 Effect of rainfall on seasonal distribution of CO2 . . . . . . 97

4.2 Tropospheric distribution of CO . . . . . . . . . . . . . . . . . . . 99

4.2.1 Sites descriptions and meteorology . . . . . . . . . . . . . 99

4.2.2 Vertical distribution of CO . . . . . . . . . . . . . . . . . . 101

4.2.3 Seasonal Cycle: Amplitude and Inter-annual Variability . . 104

4.2.4 Long-range transport of CO in the upper troposphere . . . 108

4.2.5 Comparison with model simulations . . . . . . . . . . . . . 112

4.3 Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5 Methane characteristics at an urban location 119

5.1 Measurement technique . . . . . . . . . . . . . . . . . . . . . . . . 120

5.1.1 Time series analysis . . . . . . . . . . . . . . . . . . . . . . 121

5.2 Seasonal cycle of CH4 . . . . . . . . . . . . . . . . . . . . . . . . . 123

5.3 Comparison of seasonal cycle with other sites . . . . . . . . . . . 125

5.4 Diurnal variation of CH4 . . . . . . . . . . . . . . . . . . . . . . . 127

5.5 Correlation analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 128

5.5.1 CH4-CO correlation . . . . . . . . . . . . . . . . . . . . . . 129

5.5.2 CH4-CO2 correlation . . . . . . . . . . . . . . . . . . . . . 130

5.6 Comparison with model simulations . . . . . . . . . . . . . . . . . 133

5.7 Top-down CH4 emissions . . . . . . . . . . . . . . . . . . . . . . . 136

5.8 Highlights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

6 Summary and future perspectives 141

6.1 Summary of results . . . . . . . . . . . . . . . . . . . . . . . . . . 142

6.2 Scope for future work . . . . . . . . . . . . . . . . . . . . . . . . . 146

Bibliography 149

xiv CONTENTS

List of publications 169

List of Figures

1.1 Variations in atmospheric temperature, CO2 and CH4 mixing ra-

tios in past 420,000 years. . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Variations in CO2 and CH4 mixing ratios from 1750 to 2014. . . . 3

1.3 Global mean energy flows. . . . . . . . . . . . . . . . . . . . . . . 7

1.4 Earth’s radiation spectrum. . . . . . . . . . . . . . . . . . . . . . 10

1.5 Schematic of the global CO2 budget. . . . . . . . . . . . . . . . . 12

1.6 Variations in the atmospheric mixing ratios and growth rate of

CH4 in past three decade. . . . . . . . . . . . . . . . . . . . . . . 18

1.7 Climate change drivers. . . . . . . . . . . . . . . . . . . . . . . . . 23

2.1 Schematic of NDIR technique based CO2 analyzer . . . . . . . . . 34

2.2 Schematic of CRDS technique based analyzer. . . . . . . . . . . . 35

2.3 Variation of CO2 mixing ratios with change in water vapour mixing

ratios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

2.4 Schematic of the measurement system in PRL. . . . . . . . . . . . 39

2.5 Precision and drift test of the instrument. . . . . . . . . . . . . . 41

2.6 Linearity of instrument for CO2 measurements. . . . . . . . . . . 41

2.7 Seasonal variations of averaging kernel over all the study regions. 43

2.8 Seasonal climatology of averaging kernel over different latitude and

longitude at 300 hPa. . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.1 Study location with emission map of CO2. . . . . . . . . . . . . . 56

3.2 Variations in local meteorology. . . . . . . . . . . . . . . . . . . . 56

3.3 Time series distributions of CO2 and CO. . . . . . . . . . . . . . . 59

3.4 Seasonal variations of CO2 and CO . . . . . . . . . . . . . . . . . 61

xv

xvi LIST OF FIGURES

3.5 Diurnal cycle of CO2. . . . . . . . . . . . . . . . . . . . . . . . . . 66

3.6 Diurnal variation of the biospheric flux from the Carnegie-Ames-

Stanford-Approach (CASA) ecosystem model. . . . . . . . . . . . 67

3.7 Diurnal cycle of CO . . . . . . . . . . . . . . . . . . . . . . . . . . 69

3.8 Correlations between CO and CO2 . . . . . . . . . . . . . . . . . 70

3.9 Contributions of fossil fuel and biospheric sources in the excess

diurnal cycle of CO2. . . . . . . . . . . . . . . . . . . . . . . . . . 76

3.10 Observed and ACTM model simulated diurnal cycle of CO2. . . . 79

3.11 Observed and ACTM model simulated seasonal cycle of CO2. . . 81

4.1 Seasonal variations of meteorological parameters over Delhi. . . . 89

4.2 Vertical distributions of CO2 from aircraft measurements and ACTM

simulations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

4.3 Monthly average distributions of CO2 over Delhi, India from air-

craft measurements and ACTM simulations. . . . . . . . . . . . . 93

4.4 Seasonal variations of CO2 at different tropospheric heights. . . . 94

4.5 Seasonal variations of CO2 over Delhi and Ahmedabad. . . . . . . 96

4.6 Seasonal variations of CO2 at 1 km altitude after subtracting the

background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

4.7 Study locations for the vertical distributions of CO. . . . . . . . . 100

4.8 The seasonal variations of meteorological parameters over the CO

study regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

4.9 Tropospheric distributions of CO. . . . . . . . . . . . . . . . . . . 102

4.10 Seasonal variations of CO at 900 and 300 hPa. . . . . . . . . . . . 107

4.11 Contour plots for the tropospheric distribution of CO over all the

study regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

4.12 Seasonal distribution of fire counts and CO mixing ratios at 900

hPa and 300 hPa respectively. . . . . . . . . . . . . . . . . . . . . 110

4.13 The seven day back trajectories at 10 km altitude over CO study

regions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

4.14 Comparison of CO contours from MOPITT satellite, and pseudo

retrievals of EMAC and MOZART models. . . . . . . . . . . . . . 114

LIST OF FIGURES xvii

5.1 Time series distributions of CH4 over Ahmedabad. . . . . . . . . . 121

5.2 Seasonal variations of CH4 over Ahmedabad. . . . . . . . . . . . . 123

5.3 Seasonal variations of CH4 over different locations in and around

India. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126

5.4 Climatology (2009 - 2014) of seasonal wind pattern over Indian

and surrounding regions. . . . . . . . . . . . . . . . . . . . . . . . 126

5.5 Diurnal variation of CH4 over Ahmedabad during all the four seasons.127

5.6 Correlation of CH4(exc) with CO(exc) on seasonal and diurnal scale. 130

5.7 Correlation of CH4(exc) with CO2(exc) on seasonal and diurnal scale. 132

5.8 Comparisions of the observed diurnal cycle of CH4 with ACTM

and LMDz model simulated diurnal cycles. . . . . . . . . . . . . . 134

5.9 Comparisions of the observed seasonal cycle of CH4 with with

ACTM and LMDz model. . . . . . . . . . . . . . . . . . . . . . . 135

5.10 Correlation between excess CH4(exc) and CO2(exc) mixing ratios

during the night hours (1900− 0500 hrs) of whole study period. . 137

List of Tables

1.1 The annual global fluxes of CH4 from different reservoirs. . . . . . 16

1.2 Global and South Asian emissions of CO from different sources. . 20

1.3 GWPs and lifetimes of different GHGs. . . . . . . . . . . . . . . . 24

3.1 Emission ratios of CO/CO2 (ppb/ppm) . . . . . . . . . . . . . . . 72

3.2 CO emission, estimated for the year of 2014 using top down approach. 74

3.3 Metrics of ACTM performance. . . . . . . . . . . . . . . . . . . . 82

3.4 Summary of the observations of CO2 and CO. . . . . . . . . . . . 84

4.1 Modified Normalized Mean Bias (MNMB) of ACTM relative to

aircraft observations over Delhi. . . . . . . . . . . . . . . . . . . . 95

4.2 Details of major cities inside the latitude and longitude grid box

taken for the CO study. . . . . . . . . . . . . . . . . . . . . . . . 100

4.3 Seasonal amplitude and interannual variability of CO at 900 and

300 hPa. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

5.1 Metrics of CH4 models performance. . . . . . . . . . . . . . . . . 134

xix

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List of Publications

Publications in Journals

1. Chandra, N., S. Lal, S. Venkataramani, P. K. Patra and V. Sheel (2015),

Temporal variations in CO2 and CO at Ahmedabad in western India, At-

mos. Chem. Phys. Discuss., 15, 32185-32238, doi:10.5194/acpd-15-32185-

2015.

2. Chandra, N., S. Venkataramani, S. Lal, V. Sheel and A. Pozzer (2016),

Effects of convection and long-range transport on the distribution of carbon

monoxide in the troposphere over India., Atmospheric Pollution Res.(In

press).

3. Lal, S., N. Chandra, and S. Venkataramani (2015): A study of CO2 and

related trace gases using a laser based technique at an urban site in western

India, CURRENT SCIENCE, VOL. 109, NO. 11.

4. Mallik, C., N. Chandra, S. Venkataramani and S. Lal (2016), Variability

of atmospheric carbonyl sulfide at a semi-arid urban site in western India,

Sci Total Environ., doi: 10.1016/j.scitotenv.2016.02.014.

5. Lal, S., S. Venkataramani, N. Chandra, O. R. Cooper, J. Brioude, and M.

Naja (2014), Transport effects on the vertical distribution of tropospheric

ozone over western India, J. Geophys. Res. Atmos., 119,doi:10.1002/2014JD021854.

Publications under preparation

1. Chandra, N., S. Lal, S. Venkataramani, P. K. Patra, M. Ramonet and X.

Lin, Methane at Ahmedabad: Measurements and models comparison.

169

2. Chandra, N., S. Lal, P. K. Patra and S. Venkataramani, Tropospheric

distributions of CO2 over Delhi: Airborne measurements and model simu-

lations.

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Atmos. Chem. Phys. Discuss., 15, 32185–32238, 2015www.atmos-chem-phys-discuss.net/15/32185/2015/doi:10.5194/acpd-15-32185-2015© Author(s) 2015. CC Attribution 3.0 License.

This discussion paper is/has been under review for the journal Atmospheric Chemistryand Physics (ACP). Please refer to the corresponding final paper in ACP if available.

Temporal variations in CO2 and CO atAhmedabad in western IndiaN. Chandra1,2, S. Lal1, S. Venkataramani1, P. K. Patra3, and V. Sheel1

1Physical Research Laboratory, Ahmedabad 380009, India2Indian Institute of Technology, Gandhinagar 382355, India3Department of Environmental Geochemical Cycle Research, JAMSTEC,Yokohama 2360001, Japan

Received: 29 September 2015 – Accepted: 4 November 2015 – Published: 17 November 2015

Correspondence to: S. Lal ([email protected])

Published by Copernicus Publications on behalf of the European Geosciences Union.

32185

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Abstract

About 70 % of the anthropogenic CO2 is emitted from the megacities and urban areasof the world. In-situ simultaneous measurements of carbon dioxide (CO2) and carbonmonoxide (CO) have been made using a state-of-the-art laser based cavity ring downspectroscopy technique at Ahmedabad, an urban site in western India, from November5

2013 to May 2015 with a break during March to June 2014. Annual average concen-trations of CO2 and CO have been found to be 413.0±13.7 ppm and 0.50±0.37 ppmrespectively. Both the species show strong seasonality, with lower concentrations of400.3±6.8 ppm and 0.19±0.13 ppm, respectively during the south-west monsoon, andhigher values of 419.6±22.8 ppm and 0.72±0.68 ppm, respectively in autumn (SON).10

Strong diurnal variations are also observed for both the species. The common fac-tors for diurnal cycles of CO2 and CO are the vertical mixing and rush hour traffic,while the influence of biospheric fluxes is also seen in CO2 diurnal cycle. Using COand CO2 covariation, we differentiate the anthropogenic and biospheric componentsof CO2 and found that significant contributions of biospheric respiration and anthro-15

pogenic emission in the late night (00:00–05:00 IST) and evening rush hours (18:00–22:00 IST) respectively. We compute total yearly emission of CO to be 69.2±0.07 Ggfor the study region using the observed CO : CO2 correlation slope and bottom-up CO2emission inventory. This calculated emission of CO is 52 % larger than the estimatedemission of CO by the EDGAR inventory. The observations of CO2 have been com-20

pared with an atmospheric chemistry transport model (i.e., ACTM), which incorporatesvarious components of CO2 fluxes. ACTM is able to capture the basic variabilities, butboth diurnal and seasonal amplitudes are largely underestimated compared to the ob-servations. We attribute this underestimation by model to uncertainties in terrestrialbiosphere fluxes and coarse model resolution. The fossil fuel signal from the model25

shows fairly good correlation with observed CO2 variations, which supports the overalldominance of fossil fuel emissions over the biospheric fluxes in this urban region.

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1 Introduction

Carbon dioxide (CO2) is the most important anthropogenically emitted greenhouse gas(GHG) and has increased substantially from 278 to 390 ppm in the atmosphere sincethe beginning of the industrial era (circa 1750). It has contributed to more than 65 %of the radiative forcing increase since 1750 and hence leads to the significant impact5

on the climate system (Ciais et al., 2013). Major causes of CO2 increase are anthro-pogenic emissions, especially fossil fuel combustion, cement production and land usechange. The cumulative anthropogenic CO2 emissions from the preindustrial era to2011, are estimated to be 545±85 PgC, out of which fossil fuel combustion and cementproduction contributed 365±30 PgC and land use change (including deforestation, af-10

forestation and reforestation) contributed 180±80 PgC (Ciais et al., 2013). Land andoceans are the two important sinks of atmospheric CO2, which remove about half ofthe anthropogenic emissions (Le Quéré et al., 2014). Though the global fluxes of CO2can be estimated fairly well, the regional scale (e.g. sub-continent and country level)fluxes are associated with quite high uncertainty especially over South Asian region;15

the estimation uncertainty being larger than the value itself (Patra et al., 2013; Peylinet al., 2013). Detailed scientific understanding of the flux distributions is needed forformulating effective mitigation policies (such as Kyoto Protocol). In inverse modelling,CO2 flux is estimated from atmospheric CO2 observations and using an atmospherictransport model. Therefore, it is necessary to measure CO2 concentrations covering20

different ecosystems and geographical areas of the world, which unfortunately is notthe case (Gurney et al., 2002).

Although, carbon monoxide (CO) is not a direct GHG but it affects climate and airquality through the formation of CO2 and ozone (O3). It affects the oxidizing capacityof the atmosphere through reaction with the free OH radicals. Additionally, CO can be25

used as a surrogate tracer for detecting and quantifying anthropogenic emissions fromburning processes, since it is a major product of incomplete combustion (Turnbull et al.,2006; Wang et al., 2010). The vehicular emissions contribute large fluxes of CO2 and

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CO to the atmosphere in urban regions. The verification of future mitigation activitiesdemand for quantifying the spatiotemporal distributions of these emissions. The COemissions have large uncertainty as compared to CO2, because its emission stronglydepends on the combustion efficiency, the vehicle engine and their adopted technologyas well as driving conditions. The correlation slope between the atmospheric variations5

of CO and CO2 can be used to quantify the fossil fuel contribution, distinguish betweendifferent burning processes or to determine the burning efficiency and overall trend ofanthropogenic emissions of CO in a city (Turnbull et al., 2006; Wunch et al., 2009; New-man et al., 2013; Popa et al., 2014). The CO : CO2 ratios are higher for low combustionsources (e.g. forest fires) and lower for good or efficient combustion sources (Andreae10

and Merlet, 2001; Wang et al., 2010). Further, the CO : CO2 ratio can be used for es-timating the total emission of CO over an urban area provided the total CO2 emissionis known for that area. Hence, the information about CO : CO2 ratio will be helpful tounderstand the effects on the CO emissions after adopting the newer vehicular tech-nologies and new cleaner emission norms and finally will be beneficial for reducing15

the uncertainties in CO emission inventories. Several ground based and aircraft basedcorrelation studies of CO : CO2 have been done in the past from different parts of theworld (Turnbull et al., 2006; Wunch et al., 2009; Wang et al., 2010; Newman et al.,2013) but such study has not been done in India except recently reported results fromweekly samples for three Indian sites by Lin et al. (2015).20

India is the second largest populous country in the world having about 1.3 billioninhabitants. Rapid socioeconomic developments and urbanization have made it thethird largest CO2 emitter next to China and USA since 2011 but it ranks at 137th levelbased on the per capita emission rate of CO2 (EDGAR v4.2; CDIAC – Boden et al.,2015). For example, in 2010 India’s emission rate was 2.2 t CO2 eqcapita−1 while the25

developed countries like USA, Russia and UK had emission rates of about 21.6, 17.6and 10.9 t CO2 eqcapita−1 respectively (EDGAR v4.2). The budgets of these gases onregional as well as global scales can be estimated by bottom-up and top down ap-proaches. Large uncertainties are associated in the GHGs budgets over South Asia,

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especially over India than for other continents. Based on the atmospheric CO2 inver-sion using model calculations, Patra et al. (2013) found that the biosphere in SouthAsia acted as the net CO2 sink during 2007–2008 and estimated CO2 flux of about−104±150 TgCyr−1. Further, based on the bottom-up approach, Patra et al. (2013)gave an estimate of biospheric flux of CO2 of about −191±193 TgCyr−1 for the period5

of 2000–2009. Both these approaches show the range of uncertainty 100–150 %. Oneof the major sources of these large uncertainties is the lack of spatial and temporalobservations of these gases (Law et al., 2002; Patra et al., 2013).

The first observations of CO2, CO and other greenhouse gases started in February1993 from Cape Rama (CRI) on the south-west coast of India using flask samples10

(Bhattacharya et al., 2009). After that, several other groups have initiated the mea-surements of surface level greenhouse gases (Mahesh et al., 2014; Sharma et al.,2014; Tiwari et al., 2014; Lin et al., 2015). Most of these measurements are made atweakly or fortnightly time intervals or at lower accuracy. These data are very useful forseveral studies like analyzing seasonal cycle, growth rate, and estimating the regional15

(subcontinental) carbon sources and sinks after combining their concentrations withinverse modelling and atmospheric tracer transport models. However, some importantstudies like their diurnal variations, temporal covariance . . . etc. are not possible fromthese measurements due to their limitations. Analysis of temporal covariance of atmo-spheric mixing processes and variation of flux on shorter timescales, e.g., sub-daily, is20

essential for understanding local to urban scale CO2 flux variations (Ahmadov et al.,2007; Pérez-Landa et al., 2007; Briber et al., 2013; Lopez et al., 2013; Ammoura et al.,2014; Ballav et al., 2015). Two aircraft based measurements programs namely, Civil Air-craft for the regular Investigation of the atmosphere Based on an Instrument Container(CARIBIC) (Brenninkmeijer et al., 2007) and Comprehensive Observation Network for25

TRace gases by AIrLiner (CONTRAIL) (Machida et al., 2008) have provided importantfirst look on the South Asian CO2 budget, but these data have their own limitations(Patra et al., 2011; Schuck et al., 2010, 2012). The focus of the Indigenous researchis lacking in terms of making the continuous and simultaneous measurements of CO2

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and CO over the urban regions, where variety of emission sources influence the levelof these gases.

Simultaneous continuous measurements of CO2 and CO have been made sinceNovember 2013 from an urban site Ahmedabad located in the western India usingvery highly sensitive laser based technique. The preliminary results of these measure-5

ments for one month period have been reported in (Lal et al., 2015). These detailedmeasurements are utilized for studying the temporal variations (diurnal and seasonal)of both gases, their emissions characteristics on diurnal and seasonal scale using theirmutual correlations, estimating the contribution of vehicular and biospheric emissioncomponents in the diurnal cycle of CO2 using the ratios of CO to CO2 and rough esti-10

mate of the annual CO emissions from study region. Finally, the measurements of CO2have been compared with simulations using an atmospheric chemistry-transport modelto discuss roles of various processes contributing to CO2 concentrations variations.

2 Site description, local emission sources and meteorology

The measurement facility is housed inside the campus of the Physical Research15

Laboratory (PRL), situated in the western part of Ahmedabad (23.03◦ N, 72.55◦ E,55 m a.m.s.l.) in the Gujarat state of India (Fig. 1). It is a semi-arid, urban region inwestern India, having a variety of large and small scale industries (Textile mills andpharmaceutical production facilities) in east and north outskirts. The institute is situ-ated about 15–20 km away from these industrial areas. The western part is dominated20

by the residential areas. The city has a population of about 5.6 million (Census India,2011) and has large number of automobiles (about 3.2 million), which are increasing atthe rate of about 10 %yr−1. Most of the city buses and auto-rickshaws (three-wheelers)use compressed natural gas (CNG) as a fuel. The transport-related activities are themajor contributors of various pollutants (Mallik et al., 2015). The Indo-Gangetic Plain25

(IGP) is situated in the northeast of Ahmedabad, which is very densely populated re-gion and has high levels of pollutants emitted from various industries and power plants

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along with anthropogenic emissions from burning of fossil fuels and traditional biofuels(wood, cow-dung cake etc). The Thar Desert and the Arabian Sea are situated in thenorthwest and southwest of Ahmedabad respectively.

Figure 1 shows average monthly variability of temperature, relative humidity (RH),wind speed based on data taken from Wunderground (http://www.wunderground.com),5

rainfall from Tropical Rainfall Measuring Mission (TRMM) and planetary boundary layer(PBL) height from the Modern-Era Retrospective Analysis for Research and Applica-tions (MEERA). The wind rose plot shows the surface level wind speed and directionduring different seasons over Ahmedabad in 2014. This place is known for its semi-arid climate. Large seasonal variations are observed in the wind speed and direction10

over Ahmedabad. During monsoon (June–July–August), the inter-tropical convergencezone (ITCZ) moves northward across India. It results in the transport of moist andcleaner marine air from the Arabian Sea and the Indian Ocean to the study location bysouth westerly winds, or the so-called southwest monsoon (summer monsoon). Thefirst shower due to the onset of the southwest monsoon occurs in July and it retreats15

in the mid of September over Ahmedabad. Due to heavy rain and winds mostly fromoceanic region, RH shows higher values in July, August and September. Highest RHof about 83 % is observed in September. The long-range transport of air masses fromthe northeast part of the Asian continent starts to prevail over the Indian region whenITCZ moves back southward in September and October. These months are regarded20

as transition period for the monsoon. During autumn (September–October–November),the winds are calm and undergo a change in their direction from south west to northeast. When the transition of winds takes placed from oceanic to continental region inOctober, the air gets dryer and RH decreases until December. The winds are northeasterly during winter (December- January–February) and transport pollutants mostly25

from continental region (IGP region). During pre-monsoon season (March–April–May),winds are north westerly and little south westerly which transport mixed air massesfrom continent and oceanic regions. The average wind speed is observed higher inJune and July while lower in October and March when transition of wind starts from

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oceanic to continental and continental to oceanic respectively. The monthly averagedtemperature starts increasing from January and attains maximum (34.6±1.4 ◦C) inJune, followed by a decrease until September and temperature is slightly warmer in Oc-tober compared to the adjacent months. The monthly variation in planetary boundarylayer height (PBLH) closely resembles with the temperature pattern. Maximum PBLH5

of about 1130 m is found in June and it remains in the lower range at about 500 mduring July to January. The ventilation coefficient (VC) is obtained by multiplying windspeed and PBL height which gradually increases from January to attain the maximumvalue in June and the lowest values of VC are observed in October and November.

3 Experiment and model details10

3.1 Experimental method

The measurements of ambient CO2 and CO are performed using a Picarro-G2401instrument, which is based on the wavelength scanned cavity ring down spectro-scopic (CRDS) technique. CRDS is now a well-established technique for making high-sensitivity and high precision measurements of trace gases in the ambient air, due15

to its three main characteristics (Bitter et al., 2005; Chen et al., 2010; Karion et al.,2013). First, it provides very long interaction path length (around 20 km) between thesample and the incident wavelength, by utilizing a 3-mirror configuration, which en-hances its sensitivity over other conventional techniques like Non-dispersive InfraredSpectroscopy (NDIR) and Fourier Transform Infrared Spectroscopy (FTIR). The sec-20

ond is its ability to isolate a single spectral feature with a resolution of 0.0003 cm−1,which ensures that the peak height or area is linearly proportional to the concentra-tion. The third advantage is that the measurements of trace gases using this techniqueare achieved by measuring the decay time of light intensity inside the cavity while theconventional optical absorption spectroscopy technique is based on absorption of light25

intensity. Hence, it increases the accuracy of measurements because it is insensitive

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to the fluctuations of incident light. The precision and accuracy of these measurementsfollow the WMO compatibility goals of ±0.1 ppm CO2 and ±2 ppb CO.

Figure 2 shows the schematic diagram of the measurement system, which consistsof the analyser, a glass bulb, a Nafion dryer, a heatless dryer, other associated pumpsand a set of calibration mixtures. Atmospheric air is sampled continuously from the ter-5

race of the building (20 m above the ground level) through an 1/4 inch PFA Teflon tubevia a glass manifold. An external pump is attached on one side of the glass manifoldto flush the sample line. Water vapor affects the measurements of CO2 by diluting itsmixing ratios in the air and by broadening the spectroscopic absorption lines of othergases. Although, the instrument has ability to correct for the water vapour interferes10

by using an experimentally derived water vapor correction algorithms (Crosson, 2008),but it has an absolute H2O uncertainty of ∼ 1 % (Chen et al., 2010) and can introducesa source of error using a single water vapor correction algorithm (Welp et al., 2013).This error can be minimized by either generating the correction coefficients periodicallyin the laboratory or by removing the water vapour from the sample air. Conducting the15

water vapor correction experiment is bit tricky and need extra care as discussed byWelp et al. (2013). Hence, we prefer to remove water vapour from the sample air by in-troducing a 50-strand Nafion dryer (Perma Pure, p/n PD-50T-24MSS) in the upstreamof the analyser. Nafion dryer contains a bunch of semi-permeable membrane tubingseparating an internal sample gas stream from a counter sheath flow of dry gas in20

stainless steel outer shell. The partial pressure of water vapour in the sheath air shouldbe lower than the sample air for effectively removing the water vapour from the sam-ple air. A heatless dryer generates dry air using a 4 bar compressor (KNF, MODEL:NO35ATE) which is used as a sheath flow in Nafion dryer. After drying, sample airpasses through the PTFE filter (polytetrafluoroethylene; 5 µm Sartorius AG, Germany)25

before entering the instrument cavity. This setup dries the ambient air near to 0.03 %(300 ppm) concentration of H2O. The CO2 concentrations are reported on the WMOscale, using the three calibration mixtures of CO2 (350.67±0.02,399.68±0.002 and426.20±0.006 ppm) from NOAA, Bolder USA, while the concentration of CO is re-

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ported against a calibration mixture of CO (970 ppb) from Linde UK. An additional gasstandard tank (CO2: 338 ppm, CO: 700 ppm), known as the “target”, is used to de-termine the precision of the instrument. The target tank values are calibrated againstthe CO2 and CO calibration mixtures. The target gas is introduced in the instrumentfor a period of 24 h. For CO2 and CO, the 5 min precisions were found to be 0.0155

and 0.005 ppm respectively within 1σ. Maximum drift for 24 h has been calculated bysubtracting the maximum and minimum value of 5 min average which were found tobe 0.2 and 0.015 ppm respectively for CO2 and CO. The linearity of the instrument forCO2 measurements has been checked by using three calibration standards (350.67,399.68 and 426.20 ppm) of CO2. The linearity tests are conducted very frequently and10

the slope is found in the range of 0.99–1.007 ppm with correlation coefficient (r) ofabout 0.999.

3.2 Description of AGCM-based Chemistry Transport Model (ACTM)

This study uses the Center for Climate System Research/National Institute for Envi-ronmental Studies/Frontier Research Center for Global Change (CCSR/NIES/FRCGC)15

atmospheric general circulation model (AGCM)-based chemistry-transport model(ACTM). The model is nudged with reanalysis meteorology using Newtonian relax-ation method. The U and V components of horizontal winds are used from the JapanMeteorological Agency Reanalysis (JRA-25) (Onogi et al., 2007). The model has1.125◦ ×1.125◦ horizontal resolution (T106 spectral truncation) and 32 vertical sigma-20

pressure layers up to about 50 km. Three components namely anthropogenic emis-sions, monthly varying ocean exchange with net uptake and terrestrial biospheric ex-change of surface CO2 fluxes are used in the model. The fossil fuel emissions forthe model simulations are taken from EDGAR inventory for the year of 2010. Air-sea fluxes from Takahashi et al. (2009) have been used for the oceanic CO2 tracer.25

The oceanic fluxes are monthly and are linearly interpolated between mid-months.The terrestrial biospheric CO2 tracers are provided from the Carnegie-Ames-Stanford-Approach (CASA) process model (Randerson et al., 1997), after introducing a diurnal

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variability using 2 m air temperature and surface short wave radiation from the JRA-25 as per Olsen and Randerson (2004). The ACTM simulations has been extensivelyused in TransCom CO2 model inter-comparison studies (Law et al., 2008; Patra et al.,2008).

4 Results and discussion5

4.1 Time series and general statistics

Figure 3a and c shows the time series of 30 min average CO2 and CO concentrationsfor the period of November 2013–February 2014 and July 2014 to May 2015. The con-centrations of both gases exhibit large synoptic variability because the site is close toanthropogenic sources. The concentrations and variability of both gases are observed10

lowest in the month of July and August. Maximum scatter in the concentrations andseveral plumes of very high levels both gases have been observed from October 2014until mid-March 2015. Almost all plumes of CO2 and CO are one to one correlatedand are found during evening rush hours and late nights. Figure 3e and f shows thevariations of CO2 and CO concentrations with wind speed and direction for the study15

period except July, August and September due to non-availability of wind data. Mostof the high and low concentrations of both these gases are found to be associatedwith low and high wind speeds. There is no specific direction for high levels of thesegases. This probably indicates the transport sector is an important contributor to thelocal emissions since the measurement site is surrounded by city roads.20

Figure 3b and d shows the probability distributions or frequency distributions of CO2and CO concentrations during the study period. The frequency distribution of CO2shows almost normal distribution while CO shows skewed towards right (lower con-centrations). This is because, natural cycle of the biosphere (photosynthesis and res-piration) along with some common controlling factors (local meteorology and anthro-25

pogenic sources), affects significantly the levels of CO2. The control of the boundary

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layer is common for the diurnal variations of these species because of their chemi-cal lifetimes are longer (>months) than the timescale of PBL height variations (∼h).However, biospheric fluxes of CO2 can have strong hourly variations. During the studyperiod the CO2 concentrations varied between 382–609 ppm, with 16 % of data lyingbelow 400 ppm, 50 % lying in the range 400–420 ppm, 25 % between 420–440 ppm5

and 9 % in the range of 440–570 ppm. Maximum frequency of CO2 is observed at402.5 ppm during the study period. The CO concentrations lies in the range of 0.071–8.8 ppm with almost 8 % data lies below the most probable frequency of CO at 0.2 ppm,while 70 % data lies between the concentrations of 0.21 and 0.55 ppm. Only 8 %data lies above the concentration of 1.6 ppm and rest of 14 % data lies between10

0.55 and 1.6 ppm. The annual mean concentrations of CO2 and CO are found to be413.0±13.7 ppm and 0.50±0.37 ppm respectively, after removing outliers beyond 2σvalues.

4.2 Seasonal variations of CO2 and CO

The seasonal cycles of CO2 and CO are mostly governed by the strength of emis-15

sion sources, sinks and transport patterns. Although they follow almost identical sea-sonal patterns but the factors responsible for their seasonal behaviours are distinctas for the diurnal variations. We calculate the seasonal cycle of CO2 and CO usingtwo different approaches. In first approach, we use monthly mean of all data and inthe second approach we use monthly mean for afternoon period (12:00–16:00 IST)20

only. All times are in Indian Standard Time (IST), which is 5.5 h ahead of GMT. Theseasonal cycle from first approach depicts the combined influence of local emis-sions (mostly) as well as that of large scale circulation. The second approach re-moves the auto-covariance by excluding CO2 and CO data mainly affected by localemission sources and represent seasonal cycles at the well mixed volume of the at-25

mosphere. The CO2 time series is detrended by subtracting a mean growth rate ofCO2 observed at Mauna Loa (MLO), Hawaii, i.e., 2.13 ppmyr−1 or 0.177 ppmmonth−1

(www.esrl.noaa.gov/gmd/ccgg/trends/) for clearly depicting the seasonal cycle ampli-32196

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tude. Figure 4a and b shows the variations of monthly average concentrations of CO2and CO using all daily (00:00–24:00 IST) data and afternoon (12:00–16:00 IST) data.

Both average concentrations (total and noon time) of CO2 exhibit strong seasonalcycle, but show distinct patterns (occurrence of maxima and minima) to each other.This difference occurs because seasonal cycle of CO2 from all data is mostly gov-5

erned by the PBL ventilation and large scale circulation while the seasonal cycle fromnoon time mean concentration is mostly related to the seasonality of vegetation activity.The total and noon time mean concentrations of CO show almost similar pattern andevince that the seasonal cycle of CO2 from the afternoon mean is mostly controlledby the biospheric productivity, since biospheric cycle does not influence CO concen-10

tration directly. In general, total mean values of CO2 and CO are observed lower inJuly having concentration 398.78±2.8 ppm and 0.15±0.05 ppm respectively. A sud-den increase in the total mean of both gases is observed from September to Octoberand maximum concentrations of CO2 and CO are observed to be 424.85±17 ppm and0.83±0.53 ppm, respectively, during November. From January to May the total mean15

concentration of CO2 decreases from 415.34±13.6 to 406.14±5.0 ppm and total meanconcentration of CO decreases from 0.71±0.22 to 0.22±0.10 ppm. During monsoonmonths predominance of south-westerly winds which bring cleaner air from the ArabianSea and the Indian Ocean over to Ahmedabad and high VC (Fig. 1) are responsible forthe lower concentration of total mean of both the gases. CO2 and CO concentrations20

are also at their seasonal low in the Northern Hemisphere due to net biospheric uptakeand seasonally high chemical loss by reaction with OH, respectively. In addition, deepconvections in the southwest monsoon season efficiently transport the Indian emis-sion (for CO, hydrocarbons) or uptake (for CO2) signals at the surface to the uppertroposphere, resulting in concentrations at the surface in the summer compared to the25

winter months (Kar et al., 2004; Randel and Park, 2006; Park et al., 2009; Patra et al.,2011; Baker et al., 2012). During autumn and early winter (December), lower VC val-ues cause trapping of anthropogenically emitted CO2 and CO. This is the major causefor high CO2 and CO concentrations during this period. The north-easterly winds bring

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very high levels of pollutants from IGP region and could additionally enhance the levelsof CO2 and CO during these seasons (autumn and winter). Higher VC and predomi-nance of comparatively less polluted mixed air masses from oceanic and continentalregion results in the lower total mean concentrations of both gases.

There are some clear differences which are observed in the afternoon mean concen-5

trations of CO2 as compared to daily mean. The first distinct feature is that significantdifference of about 5 ppm is observed in the afternoon mean of CO2 concentrationfrom July to August as compared to difference in total mean concentration of about∼ 0.38 ppm for the same period. Significant difference in the afternoon concentrationsof CO2 from July to August is mainly due to the increasing sink by net biospheric pro-10

ductivity after the Indian summer monsoonal rainfall. Another distinct feature is thatthe daily mean concentration of CO2 is found highest in November while the afternoonmean concentration of CO2 attains maximum value (406±0.4 ppm) in April. Prolongeddry season combined with high daytime temperature (about 41 ◦C) during April–Maymake the tendency of ecosystem to become moderate source of carbon exchange15

(Patra et al., 2011) and this could be responsible for the elevated mean noon timeconcentrations of CO2.

The average amplitude (max–min) of the annual cycle of CO2 is observed around13.6 and 26.07 ppm from the afternoon mean and total mean respectively. Differentannual cycles and amplitudes have been observed from other studies conducted over20

different Indian stations. Similar to our observations of the afternoon mean concentra-tions of CO2, maximum values are also observed in April at Pondicherry (PON) andPort Blair with amplitude of mean seasonal cycles about 7.6±1.4 and 11.1±1.3 ppmrespectively (Lin et al., 2015). Cape Rama (CRI), a costal site on the south-west coastof India show the seasonal maxima one month before than our observations in March25

with annual amplitude about 9 ppm (Bhattacharya et al., 2009). The Sinhagad (SNG)site located over the Western Ghats mountains, show very larger seasonal cycle withannual amplitude of about 20 ppm (Tiwari et al., 2014). The amplitude of mean an-nual cycle at the free tropospheric site Hanle at altitude of 4500 m is observed to be

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8.2±0.4 ppm, with maxima in early May and minima in mid-September (Lin et al., 2015).Distinct seasonal amplitudes and patterns are due to differences in regional controllingfactors for the seasonal cycle of CO2 over these locations, e.g., the Hanle is remotelylocated from all continental sources, Port Blair site is sampling predominantly marineair, Cape Rama observes marine air in the summer and Indian flux signals in the winter,5

and Sinhagad represents a forested ecosystem. These comparisons show the need forCO2 measurements over different ecosystems for constraining its budget.

The annual amplitude in afternoon and daily mean CO concentrations are observedto be about 0.27 and 0.68 ppm, respectively. The mean annual cycles of CO overPON and Port Blair show the maxima in the winter months and minima in mon-10

soon months same as our observations with annual amplitudes of 0.078±0.01 and0.144±0.016 ppm, respectively. Hence, the seasonal levels of CO are affected by largescale dynamics which changes air masses from marine to continental and vice versaand by photochemistry. The amplitudes of annual cycle at these locations differ due totheir climatic conditions and sources/sinks strengths.15

4.3 Diurnal variation

The diurnal patterns for all months and seasons are produced by first generating thetime series from the 15 min averages and then averaging the individual hours for alldays of the respective month and season after removing the values beyond 2σ stan-dard deviations for each month as outliers.20

4.3.1 Diurnal variation of CO2

Figure 5a shows bi-modal feature in the diurnal cycle of CO2 during the four seasonswith morning and evening peaks. Both peaks are associated mostly with the vehicu-lar emissions and PBL height during rush hours. There are many interesting featuresin the 00:00–08:00 IST period. Concentrations of CO2 start decreasing from 00:00 to25

03:00 IST and afterwards increases until 06:00 and 07:00 IST during monsoon and au-

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tumn. It could be mostly due to the accumulation of CO2 emitted from respiration bythe biosphere in the nocturnal boundary layer. During winter and spring the concen-trations during night hours are almost constant and increase is observed only from06:00 to 08:00 IST during winter. Dormant of respiration during these two seasons dueto lower temperature could be one of the possible factors for no increase in CO2 con-5

centrations during night. No peak during morning hours is observed in spring. Distincttimings for the occurrence of the morning peak during different seasons is generallyrelated to the sunrise time and consequently the evolutions of PBL height. The sunriseoccurs at 05:55–06:20, 06:20–07:00, 07:00–07:23 and 07:20–05:54 IST during mon-soon, autumn, winter and spring, respectively. During spring and monsoon, rush hour10

starts after sunrise, so the vehicular emissions occur when the PBL is already high andphotosynthetic activity has begun. But in winter and autumn rush hour starts parallelywith the sunrise, so the emissions occur when the PBL is low and concentration buildup is much strong in these seasons than in spring and monsoon seasons. CO2 startsdecreasing fast after these hours and attains minimum value around 16:00 IST. This15

quick drop of CO2 after sunrise is linked to the dominance of photosynthesis over therespiration processes in addition to the higher atmospheric mixing height. CO2 levelsstart increasing after 16:00 IST peak around 21:00 IST. Higher concentrations of CO2during these hours are mainly due to the rush hour vehicular emissions and less dilu-tion due to the lower PBL height. Comparative levels of CO2 during evening rush hours20

except monsoon confirm separately the major influence from the same type of sources(vehicular emission) in its levels which do not show large variability as in post-midnighthours.

The diurnal amplitude is defined as the difference between the maximum and mini-mum concentrations of CO2 in the diurnal cycle. The amplitudes of monthly averaged25

diurnal cycle of CO2 from July 2014 to May 2015 are shown in Fig. 5b. The diurnal am-plitude shows large month to month variation with increasing trend from July to Octoberand decreasing trend from October onwards. Lowest diurnal amplitude of about 6 ppmis observed in July while highest amplitude of about 51 ppm is observed in October.

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The amplitude does not change much from December to March and is observed in therange of 25–30 ppm. Similarly from April to May, the amplitude also varies in a narrowrange from 12 to 15 ppm. The jump in the amplitude of CO2 diurnal cycle is observedhighest (around 208 %) from July to August. This is mainly due to significant increase ofbiospheric productivity from July to August after the rains in Ahmedabad. It is observed5

that during July the noon time CO2 levels are found in the range of 394–397 ppm whilein August the noon time levels are observed in the range of 382–393 ppm. The lowerlevels could be due to the higher PBL height during afternoon and cleaner air, but incase of CO, average day time levels in August are observed higher than July. It rulesout that the lower levels during August are due to the higher PBL height and presence10

of cleaner marine air, and confirms the higher biospheric productivity during August.The monthly average diurnal cycles of the biospheric net primary productivity from

CASA model for Ahmedabad and for the year of 2014 are shown Fig. 6. The details ofCASA flux are given in the Sect. 3.2. It is observed that the model shows higher bio-spheric productivity in September and October while the observations are suggesting15

higher productivity in August. This indicates that the CASA model is not able to capturethe signal of higher biospheric productivity for Ahmedabad and need to be improved.Similar discrepancy in the timing of maximum biospheric uptake is also discussed ear-lier by Patra et al. (2011) using inverse model CO2 fluxes and CASA biospheric fluxes.

Near surface diurnal amplitude of CO2 has been documented in humid subtropical20

Indian station Dehradun and a dry tropical Indian station Gadanki (Sharma et al., 2014).In comparison to Ahmedabad, both these stations show distinct seasonal change inthe diurnal amplitude of CO2. The maximum CO2 diurnal amplitude of about 69 ppm isobserved during the monsoon season at Dehradun (30.3◦ N, 78.0◦ E, 435 m), whereasmaximum of about 50 ppm during autumn at Gadanki (13.5◦ N, 79.2◦ E, 360 m).25

4.3.2 Diurnal variation of CO

Figure 7a shows seasonally averaged diurnal variation of CO. In general, the meandiurnal cycles of CO during all the seasons show lower concentration during noon

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(12:00–17:00 IST) and two peaks, one in the morning (08:00–10:00 IST) and other inthe evening (18:00–22:00 IST). This cycle exhibits the same pattern as the mean diur-nal cycle of traffic flow, with maxima in the morning and at the end of the afternoon,which suggests the influence of traffic emissions on CO measurements. Along with thetraffic flow, PBL dynamics also plays a critical role in governing the diurnal cycle of CO.5

The amplitudes of the evening peaks in diurnal cycles of CO are always greater thanthe morning peaks. It is because the PBL height evolves side by side with the morningrush hours traffic and hence increased dilution while during evening hours PBL heightdecrease along with evening time rush hours traffic and favours accumulation of pol-lutants until the late evening under the stable PBL conditions. The noon time minima10

is associated with the combined influence of boundary layer dilution and loss of COdue to OH radicals. The peaks during morning and evening rush hours, minima duringafternoon hours in CO diurnal cycle during all seasons are similar as in CO2. However,there are a few noticeable differences in the diurnal cycles of both the gases. The firstnoticeable difference is that the CO morning peak appears later than CO2 peak. This is15

because as discussed earlier with sunrise time, PBL height starts to evolve and sametime photosynthesis process also gets started and hence CO2 morning peak dependson the sunshine time. But in case of CO, timing of the morning peak mostly dependson the rush hour traffic and is consistent at 08:00–10:00 IST in all seasons. The sec-ond noticeable difference is the afternoon concentrations of CO show little seasonal20

spread as compared to the afternoon concentrations of CO2. Again, this is due to thebiospheric control on the concentration of CO2 during the afternoon hours of differentseasons while CO levels are mainly controlled by the dilution during these afternoonhours. The third noticeable difference is that the levels of CO decrease very fast afterevening rush hour in all seasons while this feature is not observed in case of CO2 since25

respiration during night hours contributes to the levels of CO2. The average morning(08:00–09:00 IST) peak values of CO are observed minimum (0.18±0.1 ppm) in mon-soon and maximum (0.72±0.16 ppm) in winter while its evening peak shows minimumvalue (0.34±0.14 ppm) in monsoon and maximum (1.6±0.74 ppm) in autumn. The

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changes in CO concentrations show large fluctuations from morning peak to afternoonminima and from afternoon minima to evening peak. From early morning maxima tonoon minima, the changes in CO concentrations are found in the range of 20–200 %while from noon minima to late evening maxima the changes in CO concentrations arefound in the range of 85 to 680 %. Similar diurnal variations with two peaks have also5

been observed in earlier measurements of CO as well as NOx at this site (Lal et al.,2000).

The evening peak contributes significantly to the diurnal amplitude of CO. The largestamplitude in CO cycle is observed in autumn (1.36 ppm) while smallest amplitude is ob-served in monsoon (0.24 ppm). The diurnal amplitudes of CO are observed to be about10

1.01 and 0.62 ppm respectively during winter and spring. The monthly diurnal cycle ofCO (Fig. 7b) shows minimum (0.156 ppm) amplitude in July and maximum (1.85 ppm)in October. After October the diurnal amplitude keep on decreasing till monsoon.

4.4 Correlation between CO and CO2

The relationships between CO to CO2 can be useful for investigating the CO source15

types and their combustion characteristics in the city region of Ahmedabad. For corre-lations study, in principle the baseline levels need to be removed from the measuredconcentrations. Although, the most ideal case of determining the background levels arethe continuous measurement of respective gases at a cleaner site. But due to unavail-ability of measurements at a near by cleaner site, we use the 5th percentile value of20

CO2 and CO for each day as a background for corresponding day. The excess CO2(CO2exc) and CO (COexc) above the background for Ahmedabad city, are determinedfor each day after subtracting the background concentrations from the hours of eachday (CO2exc =CO2obs −CO2bg, COexc =COobs −CObg).

We use robust regression method for the correlation study. It is an alternative to least25

squares regression method and more applicable for analysing time series data withoutliers arising from extreme events (http://www.ats.ucla.edu/stat/stata/dae/rreg.htm).Figure 8a illustrates the correlations between COexc and CO2exc for the four seasons.

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The impact of the possible sources of CO and CO2 varies from month to month andhence season to season. The lowest correlation (r = 0.62,p = 0.0001) is observed dur-ing monsoon, with a ∆COexc/∆CO2exc ratio of 0.6±0.1 ppbppm−1. Lowest correla-tion suggest that different mechanisms control the levels of CO and CO2 during themonsoon season. As discussed previously, higher biospheric productivity during this5

season mostly controls the CO2 concentrations while CO concentrations are mostlycontrolled by the long range transport and higher loss due to OH. Highest correla-tion (r = 0.87,p < 0.0001) with ∆COexc/∆CO2exc ratio of 8.4±0.17 ppbppm−1 is ob-served during spring season. As illustrated by the diurnal cycle, the CO2 is not sig-nificantly removed by the biosphere during spring with lower draw down in daily CO2.10

Along with this, higher VC during this season will result in very fast mixing. There-fore, very fast mixing will mostly regulate their relative variation and will result in highercorrelation in this season. Other factors like soil and plant respiration during this pe-riod may also control CO2 concentrations due to which the correlation coefficient isnot equal to 1. The ratio of ∆COexc/∆COexc is estimated to be 8.5±0.15 ppbppm−1

15

(r = 0.72) and 12.7±0.17 ppbppm−1 (r = 0.74) in autumn and winter respectively. Rel-atively higher ratios during winter than other three seasons indicate contribution of COemission from additional biofuel burning sources. The winter time ratio is similar tothe airmass influenced by both fossil fuel and biofuel emissions as discussed by Linet al. (2015) over Pondicherry. Using CARIBIC observations, Lai et al. (2010) also re-20

ported the ∆CO/∆CO2 ratio in the range of 15.6–29.3 ppbppm−1 from the airmassinfluenced by both biofuel and fossil fuel burning in the Indo-Chinese Peninsula. Fur-ther, ∆CO/∆CO2 ratio is also observed of about 13 ppbppm−1 in South-east Asianoutflow in February–April 2001 during the TRACE-P campaign and suggest the com-bined influence of fossil fuel and biofuel burning (Russo et al., 2003). The narrow range25

of the ratios from autumn to spring (8.4–12.7 ppbppm−1) suggest the dominance of lo-cal emission sources during these seasons, and this range corresponds to the rangeof anthropogenic combustion sources (10–15 ppbppm−1) in developed countries (Sun-tharalingam et al., 2004; Takegawa et al., 2004; Wada et al., 2011). This suggest that

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the overall emissions of CO over Ahmedabad are mostly dominated by the anthro-pogenic combustion during these seasons.

The ∆COexc/∆CO2exc slope and their correlation may depend on the time of the daydue to the variation in different controlling factors on their levels. Hence, we computedthe diurnal cycle of ∆COexc/∆CO2exc slope for all the seasons by binning the data for5

both hour and month (3 month×24 h) as shown in Fig. 8b. The colours indicate thecorrelation coefficients (r) for respective hour. These ratios do not reflect the diurnallyvarying PBL height, but rather the diurnally varying mix of fossil fuels and biogenicsources. The ∆COexc/∆CO2exc slopes show very distinctive diurnal variation, beinghigher (30–50 ppbppm−1) in the evening rush hours with very good correlation (r >10

0.85) and lower (5–20 ppbppm−1) in the afternoon hours with lower correlation (r =0.5–0.6) during all the four seasons. Negative and lower slopes in afternoon hoursduring monsoon season indicate higher biospheric productivity during this period. Theslopes and their correlations are fairly comparable for all the four seasons in the eveningrush hours which indicate stronger influence of common emission sources. Slopes15

during this time can be considered as fresh emissions since dilution and chemicalloss of CO can be considered negligible for this time. These observed ratios are muchlower than ratios related to domestic sources but are similar transport sector mostlydominated from gasoline combustion (Table 1). Except monsoon, the overall ratios inall four seasons were found in the range of 10–25 ppbppm−1 during the daytime and20

10–50 ppbppm−1 during night-time.

4.5 Top-down CO emissions from observations

If the emissions of CO2 are known for study locations, the emissions of CO can be es-timated by multiplying the correlation slopes and molecular mass mixing ratios (Wunchet al., 2009; Wong et al., 2015). Final emissions of CO will depend on choosing the25

values of correlation slopes. The slopes should not be biased from particular localsources, chemical processing and PBL dynamics. We exclude monsoon data as theCO2 variations mainly depend on the biospheric productivity during this season. As

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discussed previously, the morning and evening rush hours data are appropriate fortracking vehicular emissions, while the afternoon data are affected by other environ-mental factors, e.g., the PBL dynamics, biospheric activity and chemical process. Thestable, shallow night-time PBL accumulates emissions since the evening and hencethe correlation slope for this period can be used as a signature of the city’s emissions.5

Hence, we calculate the slopes from the data corresponding to the period of 23:00–05:00 IST. Additionally, slopes for morning hours (06:00–10:00 IST), afternoon hours(11:00–17:00 IST), and night hours (18:00–06:00 IST) are also used for estimating theCO emissions to study the difference in the estimation of CO emissions due to choos-ing different times for slopes. The CO emission (ECO) for Ahmedabad is calculated10

using the following formula.

ECO =

(αCO

MCO

MCO2

)ECO2

(1)

Where, αCO is the correlation slope of COexc to CO2exc ppb ppm−1, MCO is themolecular mass of CO in g mol−1, MCO2

is the molecular mass of CO2 in g mol−1

and ECO2is the CO2 emission in Gigagram (Gg) over Ahmedabad. The EDGARv4.215

emission inventory reported an annual emissions of CO2 at 0.1◦ ×0.1◦ for the pe-riod of 2000–2008 (http://edgar.jrc.ec.europa.eu/overview.php?v=42). It reported anannual CO2 emission of 6231.6 Gg CO2 year−1 by EDGARv4.2 inventory over the box(72.3< longitude<72.7◦ E, 22.8< latitude<23.2◦ N) which contain Ahmedabad coordi-nates in center of the box. We assume that the emissions of CO2 are linearly changing20

with time and using increasing rate of emission from 2005 to 2008, we extrapolatethe emission of CO2 for 2014 over same area. The bottom-up CO2 emission for theAhmedabad is thus estimated of about 8368.6 Gg for the year of 2014. Further, forcomparing the estimated emission with inventory emissions we extrapolated the COemissions also for the year of 2014 using same method applied as for CO2. Further,25

we assumed same slopes for the year of 2008 and calculate CO emission for that year

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also. The slope values for different time period, estimated and inventory emissions ofCO using different values of slope are given in Table 2.

The correlation between COexc and CO2exc for the period of 23:00–05:00 IST is verytight and slope for this period can be considered for estimating the fossil fuel CO emis-sions for Ahmedabad. Using this slope and based on CO2 emissions from EDGAR5

inventory, the estimated fossil fuel emission for CO is observed to be 69.2±0.7 Gg forthe year of 2014. The EDGAR inventory underestimates the emission of CO as theygive the estimate of about 45.3 Gg extrapolated for 2014. The slope corresponding tothe night hours (18:00–06:00 IST) gives the highest estimate of CO. Using all combi-nations of slopes, the derived CO emissions are larger than the bottom-up EDGAR10

emission inventory.

4.6 Diurnal tracking of CO2 emissions

CO has virtually no natural sources in an urban environment except oxidation of hydro-carbons. As we discussed earlier that incomplete combustion of fossil fuels is the mainsource of CO in urban environments and therefore can be used as a surrogate tracers15

to attribute CO2 enhancements to fossil fuel combustion on shorter timescale. Severalstudies have demonstrated that the ratio of the excess concentrations of CO and CO2in background concentrations can be used to determine the fraction of CO2 from fossilfuels and validated this method using carbon isotope (∆14CO2) measurements (Levinet al., 2003; Turnbull et al., 2006, 2011; Lopez et al., 2013; Newman et al., 2013). This20

quantification technique is more practical, less expensive and less time consuming incomparison to the 14CO2 method (Vogel et al., 2010). For performing this analysis, thebackground concentrations of CO and CO2 and the emission ratio of CO/CO2 fromanthropogenic emissions are required. The methods for calculating the backgroundconcentrations of CO2 and CO are already discussed in Sect. 4.4. Figure 9a shows25

the excess diurnal variations of CO2 above the background levels during different sea-sons. As discussed in the previous section, the vehicular emissions are major emissionsources over the study locations. For calculating the emission ratio of CO/CO2 from

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the vehicular emissions, we used the evening time (19:00–21:00 IST) concentrations ofCO2exc and COexc for whole study period since correlation for this period is very high.The other reason for choosing this time is that there is insignificant contribution of bio-spheric CO2 and no chemical loss of CO. We assume that negligible influence of othersources (open biomass burning, oxidation of hydrocarbons) during this period. The5

emission ratio for this time is calculated to be about 47±0.27 ppb CO ppm−1 CO2 withvery high correlation (r = 0.95) (Fig. 9b) after excluding those data points, correspond-ing for which the mean wind speed is greater than 3 ms−1 for avoiding the effect of fastventilation and transport from other sources. The tight correlation imply that there isno substantial difference in the emission ratio of these gases during the measurement10

period from November 2013 to May 2015. CO2exc and COexc will be poorly correlatedwith each other if their emission ratio varies largely with time, assuming the correlationis mainly driven by emissions. Since this ratio is mostly dominated by the transportsector, this analysis will give mainly the fraction of CO2 from the emissions of transportsector. We define it as RCO/CO2veh

. The standard deviation shows the uncertainty asso-15

ciated with slope which is very small. The contribution of transport sector (CO2Veh) inthe diurnal cycle of CO2 is calculated using the following formula.

CO2Veh =COobs −CObg

RCO/CO2veh

(2)

where COobs is the observed CO concentration and CObg is a background CO value.Uncertainty in the CO2Veh is dominated by the uncertainty in the RCO/CO2veh

and by the20

choice of CObg. The uncertainty in CO2Veh due to the uncertainty in the RCO/CO2vehis

about 0.5 % or 0.27 ppm and can be considered negligible. As discussed in Sect. 3,the uncertainty in the measurements of CObg is very small and also can be considerednegligible. Further, the contributions of CO2 from other major sources are calculatedby subtracting the CO2Veh from the excess concentrations of CO2. These sources are25

those sources which do not emit significant amount of CO and can be consideredmostly as natural sources (respiration), denoted by CO2bio.

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The average diurnal cycles of CO2 above its background for each seasons are shownin (Fig. 9a). The diurnal pattern of CO2Veh (Fig. 9c) reflects the pattern like CO, becausewe are using constant RCO/CO2veh

for all seasons. Overall, this analysis suggests thatthe anthropogenic emissions of CO2 from transport sectors during early morning from06:00 to 10:00 IST varied from 15 to 60 % (4–15 ppm). During afternoon h (11:00–5

17:00 IST), the vehicular emitted CO2 varied from 20 to 70 % (1–11 ppm) and duringevening rush hours (18:00–22:00 IST), it varies from 50 to 95 % (2–44 ppm). Duringnight/early morning hours (00:00–07:00 IST) respiration contributes from 8 to 41 ppmof CO2 (Fig. 9d). The highest contributions from 18 to 41 ppm are observed in theautumn from the respiration sources during night hours, since there is more biomass10

during this season after the South Asian summer monsoon. During afternoon hours,lower biospheric component of CO2 could be due to a combination of the effects ofafternoon anthropogenic emissions, biospheric uptake of CO2 and higher PBL height.

4.7 Model – observations comparison

4.7.1 Comparison of diurnal cycle of CO215

We first evaluate the ACTM in simulating the mean diurnal cycle of CO2 over Ahmed-abad by comparing model simulated surface layer mean diurnal cycle of CO2. Theatmospheric concentrations of CO2 are calculated by adding the anthropogenic com-ponent, oceanic component, biospheric component from CASA process model. Fig-ure 10a and b shows the residuals (Hourly mean – daily mean) of diurnal cycles of20

CO2 based on the observations and model simulations respectively. Model shows verylittle diurnal amplitude as compared to observational diurnal amplitude. Larger differ-ences and discrepancies in night time and morning CO2 concentrations between themodel and observations might be contributed by diurnal cycle of the anthropogenicfluxes from local emissions and biospheric fluxes, and uncertainties in the estimation of25

PBLH by the model. Hence, there is a need for efforts in improving the regional anthro-pogenic emissions as well as module for estimating the PBL height. It may be pointed

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out that the model’s horizontal resolution (1.125◦ ×1.125◦) is coarse for analysing localscale observations. However, model is able to capture the trend of the diurnal ampli-tude, highest in autumn and lowest in monsoon. Figure 10c shows better agreement(r = 0.75) between the monthly change in model and observational diurnal amplitudeof CO2 from monthly mean diurnal cycle however slope (m = 0.17) is very poor. We5

include the diurnal amplitudes of CO2 for November and December 2013 also for im-proving the total number of data points. The model captured the spread in the day timeconcentration of CO2 from monsoon to spring with a difference that model shows lowerconcentration of CO2 during noon hours in autumn while observations show lowest inmonsoon. Most of the atmospheric CO2 uptake occur following the Southwest mon-10

soon season during July–September (Patra et al., 2011) and as a consequence, weobserve the lowest CO2 concentration from the measurements during this season. Butmodel is not able to capture this feature since CASA biospheric flux (Fig. 6) showshighest productivity in autumn and hence lowest concentrations of CO2 in autumn dur-ing daytime. This also suggest that there is a need for improving the biospheric flux15

for this region. It should be mentioned here that CASA model used a land use mapcorresponding the late 1980s and early 1990s, which should be replaced by rapidgrowth in urbanised area in Ahmedabad (area and population increased by 91 and42 %, respectively, between 1990 and 2011). The model resolution may be anotherfactor for discrepancy. As Ballav et al. (2012) show that a regional model WRF-CO220

is able to capture both diurnal and synoptic variations at two closely spaced stationswithin 25 km. Hence the regional models could be helpful for capturing these variabili-ties.

4.7.2 Comparison of seasonal cycle of CO2

Figure 11a shows the performance of ACTM simulating mean seasonal cycle of CO225

over Ahmedabad by comparing model simulated mean surface seasonal cycle of CO2.Due to unavailability of data from March 2014 to June 2014 we plotted the monthly av-erage of the year 2015 for same periods for visualizing the complete seasonal cycle of

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CO2. The seasonal cycles are calculated after subtracting the annual mean from eachmonth, and corrected for growth rate using the observations at MLO. For comparison,we use the seasonal cycle calculated from afternoon time average monthly concen-trations, since model is not able to capture the local fluctuations and produce betteragreements when boundary layer is well mixed. In Table 3 we present the summary5

of the comparisons of model and observations. The model reproduces the observedseasonal cycle in CO2 fairly well but with low seasonal amplitude about 4.15 ppm com-pared to 13.6 ppm observed. Positive bias during monsoon depicts the underestimationof biospheric productivity by CASA model. The root mean square error is observedhighest to be 3.21 % in monsoon. For understanding the role of biosphere, we also10

compared the seasonal cycle of CO2 from noon time mean data with the seasonal cy-cle of CO2 fluxes over South Asia region which is taken from the Patra et al. (2011)where they calculated it using an inverse model by including CARIBIC data and shifteda sink of 1.5 Pg C yr−1 from July to August and termed it as “TDI64/CARIBIC-modified”.Positive and negative values of flux show the net release and net sink by the land bio-15

sphere over the South Asia. This comparison shows almost one to one correlation inthe monthly variation of CO2 and suggest that the lower levels of CO2 during July, Au-gust and higher level in April are mostly due to the moderate source and sink of SouthAsian ecosystem during these months respectively. Significant correlation (r = 0.88)between South Asian CO2 fluxes and monthly mean CO2 data for day time only sug-20

gest that the day time levels of CO2 are mostly controlled by the seasonal cycle ofbiosphere (Fig. 11b).

Separate correlation between individual tracers of model and observed data hasbeen studied to investigate the relative contribution of individual tracer component inthe CO2 variation (Fig. 11b). We did not include the oceanic tracer and observed CO225

correlation result, since no correlation has been observed between them. The com-parison is based on daily mean of entire time series. Correlation between biospherictracers and observed CO2 have been found negative. This is because during growingseason biospheric sources act as a net sink for CO2. Correlation of observed CO2 with

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fossil fuel tracer has been observed fairly well (r = 0.75). Hence, individual tracers cor-relation study also gives the evidence of the overall dominance of fossil flux in overallconcentrations of CO2 over Ahmedabad for entire study period, and by assuming fossilfuel CO2 emission we can derive meaningful information on biospheric uptake cycle.

This study suggests that the model is able to capture seasonal cycle with lower5

amplitude for Ahmedabad. However, the model fails to capture the diurnal variabilitysince local transport and hourly daily flux play important roles for governing the diurnalcycle and hence there is a need for improving these features of the model.

5 Conclusions

We report simultaneous in-situ measurements of CO2 and CO concentrations in the10

ambient air at Ahmedabad, a semiarid urban region in western India using laser basedCRDS technique during 2013–2015. The unique flow of air masses originating fromboth polluted continental regions as well as cleaner marine regions over the studylocation during different seasons make this study most important for studying the char-acteristics of both polluted and relatively cleaner air masses. Several key results are15

presented in this study. The observations show the range of CO2 concentrations from382 to 609 ppm and CO concentrations from 0.07 to 8.8 ppm, with the average of CO2and CO to be 416±19 ppm and 0.61±0.6 ppm respectively. The highest concentrationsof both the gases are recorded for lower ventilation and for winds from north-east di-rection, representing CO2 and CO transported from anthropogenic sources. The lowest20

concentrations of both the gases are observed for higher ventilation and for the south-west direction, where air travels from the Indian Ocean. Along with these factors, thebiospheric seasonal cycle (photosynthesis outweighs respiration during growing sea-son and reverse during fall season) also controls the seasonal cycle of CO2. Lowestday time CO2 concentrations ranging from 382 to 393 ppm in August, suggest for the25

stronger biospheric productivity during this month over the study region, in agreementwith an earlier inverse modelling study. This is in contrast to the terrestrial flux simu-

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lated by the CASA ecosystem model, showing highest productivity in September andOctober months. Hence, the seasonal cycles of both the gases reflect the seasonalvariations of natural sources/sinks, anthropogenic emissions and seasonally varyingatmospheric transport. The annual amplitudes of CO2 variation after subtracting thegrowth rate based on the Mauna Loa, Hawaii data are observed to be about 26.07 ppm5

using monthly mean of all the data and 13.6 ppm using monthly mean of the afternoonperiod (12:00–16:00 IST) data only. Significant difference between these amplitudessuggests that the annual amplitude from afternoon monthly mean data only does notgive true picture of the variability. It is to be noted that most of the CO2 measurementsin India are based on day time flask samplings only.10

Significant differences in the diurnal patterns of CO2 and CO are also observed,even though both the gases have major common emission sources and effects of PBLdynamics and advection. Differences in their diurnal variability is probably the effectof terrestrial biosphere on CO2 and chemical loss of CO due to reaction with OH rad-icals. The morning and evening peaks of CO are affected by rush hours traffic and15

PBL height variability and occur almost same time throughout the year. However, themorning peaks in CO2 changes its time slightly due to shift in photosynthesis activityaccording to change in sun rise time during different seasons. The amplitudes of annualaverage diurnal cycles of CO2 and CO are observed about 25 and 0.48 ppm respec-tively (Table 4). Both gases show highest amplitude in autumn and lowest in monsoon.20

This shows that major influencing processes are common for both the gases, specificto this city and the monsoon India.

The availability of simultaneous and continuous measurements of CO2 and COhave made it possible to study their correlations during different times of the dayand during different seasons. The minimum value of slope and correlation coefficient25

of 0.8±0.2 ppbppm−1 and 0.62 respectively are observed in monsoon. During otherthree seasons, the slopes vary in narrow range (Table 4) and indicate about the com-mon emission sources of CO during these seasons. These slopes lie in the range(10–15 ppbppm−1) of anthropogenic sources in developed countries, e.g., Japan. This

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suggest that the overall emissions of CO over Ahmedabad are mostly dominated bythe anthropogenic (fossil fuel) combustion. These slopes also show significant diurnalvariability having lower values (about 5–20 ppbppm−1) during noon hours and highervalues (about 30–50 ppbppm−1) during evening rush hours with highest correlation(r > 0.9). This diurnal pattern is similar to the traffic density and indicate the strong5

influence of vehicular emissions in the diurnal pattern of CO. Further, using the slopefrom the evening rush hours (18:00–22:00 IST) data as vehicular emission ratios, thecontributions of vehicular emissions and biospheric emissions in the diurnal cycle ofCO2 have been segregated. At rush hours, this analysis suggests that 90–95 % of thetotal emissions of CO2 are contributed by vehicular emissions. Using the relationship,10

the CO emission from Ahmedabad has been estimated. In this estimation, fossil fuelderived emission of CO2 from EDGAR v4.2 inventory is extrapolated linearly from 2008to 2014 and it is assumed that there are no year-to-year variations in the land bioticand oceanic CO2 emissions. The estimated annual emission CO for Ahmedabad isestimated to be 69.2±0.7 Gg for the year of 2014. The extrapolated CO emission from15

EDGAR inventory for 2014 shows a value smaller than this estimate by about 52 %.The observed results of CO2 are also compared with an atmospheric general cir-

culation model based chemistry transport model simulated CO2 concentrations. Themodel captures some basic features like the trend of diurnal amplitude, seasonal am-plitude etc, qualitatively but not quantitatively. The model captures the seasonal cycle20

fairly good but the amplitude is very less as compared to the observations. Similarly,performance of the model capturing the change in monthly averaged diurnal ampli-tude is quiet good (r = 0.72), however the slope is very poor. We also examined thecorrelation between the hourly averaged observed CO2 and tracer of fossil fuel frommodel simulation and found fairly good correlation between them. However, no signif-25

icant correlation has been observed between observed CO2 and biospheric tracer. Itsuggests that the levels of CO2 over Ahmedabad are mostly controlled by the fossil fuelcombustion throughout the year.

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This work demonstrates the usefulness of simultaneous measurements of CO2 andCO in an urban region. The anthropogenic and biospheric components of CO2 havebeen studied from its temporally varying atmospheric concentrations, and validity of“bottom-up” inventory has been assessed. Use of COexc : CO2exc ratios avoid some ofthe problems with assumptions that have to be made with modelling. These results5

represent a major urban region of India and will be helpful in validating emission in-ventories, chemistry-transport and terrestrial ecosystem models. However, a biggernetwork of sites is needed to elucidate more accurate distribution of emissions andtheir source regions, and run continuously over multiple years for tracking the changesassociated with anthropogenic activities and emission mitigation policies.10

Acknowledgements. We thank PRL and ISROGBP-ATCTM for funding and support. We ac-knowledge the support of T. K. Sunil Kumar in making the measurements.

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Turnbull, J. C., Karion, A., Fischer, M. L., Faloona, I., Guilderson, T., Lehman, S. J., Miller, B. R.,20

Miller, J. B., Montzka, S., Sherwood, T., Saripalli, S., Sweeney, C., and Tans, P. P.: Assess-ment of fossil fuel carbon dioxide and other anthropogenic trace gas emissions from airbornemeasurements over Sacramento, California in spring 2009, Atmos. Chem. Phys., 11, 705–721, doi:10.5194/acp-11-705-2011, 2011. 32207

Vogel, F. R., Hammer, S., Steinhof, A., Kromer, B., and Levin, I.: Implication of weekly25

and diurnal 14C calibration on hourly estimates of CO-based fossil fuel CO2 at a mod-erately polluted site in southwestern Germany, Tellus B, 62, 512–520, doi:10.1111/j.1600-0889.2010.00477.x, 2010. 32207

Wada, A., Matsueda, H., Sawa, Y., Tsuboi, K., and Okubo, S.: Seasonal variation of enhance-ment ratios of trace gases observed over 10 years in the western North Pacific, Atmos. Env-30

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its correlation with CO at a rural site near Beijing: implications for combustion efficiency in

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China, Atmos. Chem. Phys., 10, 8881–8897, doi:10.5194/acp-10-8881-2010, 2010. 32187,32188

Welp, L. R., Keeling, R. F., Weiss, R. F., Paplawsky, W., and Heckman, S.: Design and per-formance of a Nafion dryer for continuous operation at CO2 and CH4 air monitoring sites,Atmos. Meas. Tech., 6, 1217–1226, doi:10.5194/amt-6-1217-2013, 2013. 321935

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Wong, K. W., Fu, D., Pongetti, T. J., Newman, S., Kort, E. A., Duren, R., Hsu, Y.-K., Miller, C. E.,Yung, Y. L., and Sander, S. P.: Mapping CH4 : CO2 ratios in Los Angeles with CLARS-FTS10

from Mount Wilson, California, Atmos. Chem. Phys., 15, 241–252, doi:10.5194/acp-15-241-2015, 2015. 32205

Wunch, D., Wennberg, P. O., Toon, G. C., Keppel-Aleks, G., and Yavin, Y. G.: Emissionsof greenhouse gases from a North American megacity, Geophys. Res. Lett., 36, L15810,doi:10.1029/2009GL039825, 2009. 32188, 3220515

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Table 1. Emission ratios of CO/CO2 (ppb ppm−1), derived from emission factors (gram of gasesemitted per kilogram of fuel burned).

Biomass burning Transport Industry Domestic

Crop-residuea,b,c Dieseld,e,f Gasolined,f Coal Coald,f Biofuelc,d

45.7–123.6 8.6–65.2 33.5 23.5–40.4 53.3–62.2 52.9–98.5

a Dhammapala et al. (2007). b Cao et al. (2008). c Andreae and Merlet (2001). d Streets et al. (2003).e Sánchez-Ccoyllo et al. (2009). f Westerdahl et al. (2009).

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Table 2. Estimates of emissions of CO using the the CO2 emission from EDGAR inventoryover the box (72.3< longitude<72.7◦ E, 22.8< latitude<23.2◦ N) and observed COexc : CO2excslopes for different time periods. The correlation coefficient for corresponding slopes are giveninside the bracket in slope column. Monsoon data are not included for calculating slopes.

Time (IST) Slope (ppb ppm−1)Correlation coefficient (r)

EDGAR Emissions(Gg yr−1)

Estimated EmssionsGg (yr)

CO2 CO

23:00–05:00 IST 13±0.14(0.84) 69.2±0.7

06:00–10:00 IST 11.4±0.19(0.75) 60.7±1.0

11:00–16:00 IST 14.9±0.19(0.78) 8368.6 45.3 79.3±1.0

18:00–05:00 IST 34.6±0.37(0.76) 184.2±1.9

Full day (24 h) 10.8±0.09(0.73) 57.5±0.5

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Table 3. Model performance matrices used to quantify the level of agreement between modelsimulations and observations. These statistics are based on hourly values in each day.

Parameter Winter Autumn Monsoon All months

MB (ppm) −2.72 12.64 −2.45 2.27FGE (%) 0.96 3.12 2.0 1.76RMSE (ppm) 5.21 12.82 9.14 8.60RMSE (%) 1.27 3.21 2.20 2.09

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Table 4. Summary of results for the study period. The total refers to the average of all the 24 hdata while noon time values refer to the average for 12:00–16:00 IST. The “exc” refer to theexcess concentrations of CO2 and CO after subtracting the background concentrations.

Period Mean (ppm) Diurnal amplitude(ppm)

CorrelationCOexc : CO2exc

CO2 CO CO2 CO Slope(ppb ppm−1)

Correlationcoefficient (r)

Monsoon 400.3±6.8 0.19±0.13 12.4 0.24 0.6±0.1 0.62Autumn 419.6±22.8 0.72±0.71 40.9 1.36 8.5±0.2 0.72Winter 417.2±18.5 0.73±0.68 31.7 1.01 12.7±0.2 0.71Spring 415.4±14.8 0.41±0.40 15.9 0.62 8.4±0.1 0.87Annual 413.0±13.7 0.50±0.37 25.0 0.48 8.3±0.7 0.79

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Figure 1. (A1) Spatial distribution of total anthropogenic CO2 emissions from EDGARv4.2 in-ventory over Ahmedabad and surrounding regions. (A2) The Ahmedabad city map showinglocation of the experimental site (PRL). (A3: a–d) Monthly average temperature with monthlymaximum and minimum value, relative humidity (RH), rainfall, wind speed, PBL height andventilation coefficient (VC) over Ahmedabad during the year 2014. Temperature, RH and windspeed are taken from the Wunderground weather (www.wunderground.com) while rainfall andPBLH data are used from Tropical Rainfall Measuring Mission (TRMM) satellite and MEERAreanalysis data. (A4) Wind rose plots for Ahmedabad for the four seasons of 2014 using dailyaverage data from the Wunderground.

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CRDS

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Figure 2. Schematic diagram of the analysis system. We introduced additionally a Nafion dryerin the inlet of instrument for removing the water vapour from the ambient air. The calibrationmixtures (three) from NOAA, USA are used to calibrate CO2 measurements and one calibrationmixture from Linde, UK is used to calibrate CO measurements.

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Figure 3. (a, c) Time series of 30 min average values CO2 and CO measured at Ahmedabad forthe study period. (b, d) The frequency distribution in CO2 and CO concentrations for the studyperiod using 30 min mean of both gases. (e, f) The polar plots show the variation of 30 minaveraged CO2 and CO at this site with wind direction and speed during the study period exceptJuly, August and September due to unavailability of meteorology data.

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Figure 4. The seasonal variation of CO2 and CO at Ahmedabad, India from July 2014 to May2015 using their monthly mean concentrations. The blue dots and red rectangles show themonthly average concentrations of these gases for the total (00:00–24:00 IST) and noon time(12:00–16:00 IST) data respectively with 1σ spread. All times are in Indian Standard Time (IST),which is 5.5 h ahead of GMT.

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Figure 5. (a) Average diurnal variation of CO2 over Ahmedabad during all the four seasons. (b)Monthly variation of average diurnal amplitude of CO2 from July 2014 to May 2015.

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Figure 6. Diurnal variation of biospheric fluxes from the CASA ecosystem model.

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Figure 7. (a) Diurnal variation of CO over Ahmedabad during all the four seasons. (b) Monthlyvariation of the diurnal amplitude of CO.

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Figure 8. (a) Correlation of excess CO above background (COexc) with the excess CO2 abovebackground (CO2exc) during all the four different seasons. Each data points are averaged for30 min. (b) The diurnal variation of the ∆COexc/∆COexc slopes during all the four seasons. Thecolour bar in this plot shows the correlation coefficient (r) for corresponding time.

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r = 0.95

Figure 9. (a) Diurnal cycle of excess CO2 over background levels during all the four seasons.(b) Correlation between excess CO and CO2 for evening hours (18:00–21:00 IST) during thestudy period. Contributions of fossil fuel (c) and biosphere (d) in the diurnal variation of excessCO2 in all the four seasons.

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Figure 10. Residual of the diurnal cycle of CO2 (in ppm) for (a) observations and (b) modalsimulation over Ahmedabad in all the four seasons. Please note that the scales of model andobservational diurnal cycles are different. (c) Correlation between observed and model simu-lated monthly mean diurnal cycle amplitudes.

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Figure 11. (a) The red circles and blue triangles show the mean seasonal cycles of CO2 (inppm) using afternoon values only, calculated from measurements and model over Ahmedabad.The green triangles show the seasonal cycles of CO2 flux over South Asia, calculated fromTDI64/CARIBIC-modified inverse model as given in Patra et al. (2011) (Fig. 3d). (b) Blue barand red bar shows the correlation coefficient (r) of model CO2 concentration of biospheric tracerand fossil fuel tracer component with observed concentrations of CO2 taking the entire annualtime series of daily mean data, respectively. The green bar shows the correlation coefficientbetween the monthly residuals of afternoon mean only and the CO2 flux over South Asia.

32238

RESEARCH COMMUNICATIONS

CURRENT SCIENCE, VOL. 109, NO. 11, 10 DECEMBER 2015 2111

*For correspondence. (e-mail: [email protected])

A study of CO2 and related trace gases using a laser-based technique at an urban site in western India S. Lal1,*, N. Chandra1,2 and S. Venkataramani1 1Physical Research Laboratory, Ahmedabad 380 009, India 2Indian Institute of Technology, Gandhinagar 382 424, India Continuous measurements of surface-level carbon di-oxide (CO2) along with its co-emitted carbon monoxide (CO) and methane (CH4) are being made at Ahmedabad using a laser-based cavity ring down spectrometer, which offers much longer path length for accurate and fast measurements of these species simultaneously. The average data during November 2013 show large variability in all the three species. These measurements also show significant diurnal variations with maximum in CO being relatively the shortest-lived species in this set of gases. The correla-tions and slopes among them have been used to iden-tify potential emission sources. Keywords: Cavity ring down spectroscopy, fossil fuel, greenhouse gases, India, vehicular emissions. CARBONDIOXIDE (CO2) is the most important greenhouse gas (GHG) of anthropogenic origin. It is found to in-crease with time due to increasing use of fossil fuel com-bustion and biomass burning. The present level of background CO2 has crossed 400 ppm and it is currently increasing at an average rate of 2.13 ppmv per year (in 2014) based on the Mauna Loa data (www.esrl.noaa.gov/ gmd/ccgg/trends). Recent measurements show that the amplitude of seasonal variation of atmospheric CO2 in the northern hemisphere has increased since 1950s (ref. 1). Effects of rising levels of this gas as well as other GHGs like CH4 and N2O are clearly evident in rising air tem-perature and associated effects all over the globe2, as well as in the Indian region3–5. The Asian countries, including India and China are growing fast, which leads to increase in the emissions of these gases. Since the lifetimes of these species are high, the effects are going to be felt all over the globe irrespective of the emission locations. Sources and sinks of these GHGs need to be identified and estimated accurately to understand their variability and future trends. The budgets of these gases can be estimated using top-down and bottom-up approaches. However, both approaches show larger uncertainties (100–150%) in the biospheric flux of CO2 over South Asia than other continents, as shown by Patra et al.6,7. One of the major sources for these larger uncertainties is the lack of measurements with required temporal and spa-tial resolutions. There are several groups in the developed

countries making such measurements, but these are lack-ing in India. Hence, there is a need for an observational network covering different ecosystems in India to make precise measurements of these gases in order to under-stand their variability, trends and improved understanding of their emission budgets in the Indian region. Since CO2 is also related to the biosphere (photosynthesis and respi-ration), its measurements in different ecosystems are necessary. Several groups are now involved in the meas-urement of surface-level CO2 in India8–12. Some of them are also making measurements of CO2 flux using tower measurements13–15. However, there are limited measure-ments of CO2 with other co-emitted gases like carbon monoxide (CO), methane (CH4), etc. Simultaneous meas-urements of CO2 with CO and CH4 have their importance in terms of constraining emission sources and their na-ture. CO2 measurements alone can give information on basic features in terms of diurnal variation, seasonal vari-ation and their amplitudes, but cannot give information related to source types. Interrelations among these gases can yield important information on the type of emission sources. This communication presents some initial results from a recently initiated project as a part of the ISRO-GBP to study the levels, variability and trends in CO2 and related trace gases at Ahmedabad. There are many techniques to measure CO2 in air. The collected air samples can be analysed using a gas chro-matograph equipped with a flame ionization detector and a Ni-catalyst to convert CO2 to methane, which is finally detected. Though the system is rugged, it has limitation related to portability, precision and accuracy. Infrared absorption-based (using non-dispersive infrared NDIR technique) analysers are highly portable with better sensi-tivity and accuracy. These systems have been extensively used for field studies, including aircraft-based measure-ments16,17. However, these have the problem of drift (zero and temperature drift) in the system and need frequent calibration. This problem is overcome in the systems based on cavity ring down spectroscopy (CRDS) tech-nique. This technique is based on the measurement of the rate of absorption rather than the magnitude of absorption of a laser pulse confined in a closed optical cavity filled with the sample air. Since many atmospheric gases have absorption bands in the near-infrared region, it is possible to tune the laser wavelength for a specific gas molecule. Since the effective path length inside the cavity becomes very large (many kilometres), the sensitivity of this sys-tem becomes extremely good. It can measure gases in parts per billion level with high accuracy and with very fast response in seconds or less18,19. We have used an ana-lyser based on this technique for the simultaneous meas-urements of CO2, CO, CH4 and water vapour (Picarro, G2401). The precisions for a 5 min average are ∼50, ∼2 and ∼0.7 ppb for CO2, CO and CH4 respectively. It uses a pump to suck the inlet air through the analyser. Since we are not interested in measuring water vapour and to avoid



its calibration and correction to calculate dry mole frac-tions, we remove water in the sample air before it enters into the system using a Nafion dryer (100 tubes, 24 inch length). It brings down the water level to about 0.03% from 2% to 3%. This analyser is frequently calibrated using three CO2 mixtures from NOAA, USA at 350, 400 and 425 ppmv. The analyser is also calibrated for CO and CH4 using gas mixtures from Linde, UK. The measurements are being made from the sixth floor of the main building of the Physical Research Laboratory (PRL), Ahmedabad (23N, 72.5E, 49 m amsl). It is located in the western region of the city, dominated by various educational organizations and residential socie-ties. A power house and some industries are located in the eastern parts of the city. The city has a population of about 50 lakhs and about 32 lakh registered vehicles, which are growing by about 10% per year. All these stationary and mobile fossil-fuel burning systems are a good source of gaseous and particulate pollutants. The city has a warm climate with high temperatures going up to 44C in May and lowest temperature of about 8C in January. The wind is mostly from the west except during the monsoon season, when it becomes southwest. Cool air during the winter comes from the northwest direction. The average annual rainfall is about 780 mm, which mostly occurs in July and August. Figure 1 shows air tem-perature, relative humidity, wind direction and wind speed, and solar radiation received at the ground in November 2013. These measurements are made using a weather sta-tion located in the PRL campus. There are some breaks in the data due to technical problems. The air temperature during this month was in the range 17–33C. The relative humidity was in the range 25–75%. Wind speed varied from 1 to 4 m s–1 and the direction changed from north-east to southwest. The relative humidity as well as the

Figure 1. Variability of meteorological parameters measured using a weather station located at PRL, Ahmedabad.

solar radiation showed slight decreasing trend during this month. Continuous in situ surface-level observations of CO2, CO and CH4 using this system started at PRL in Novem-ber 2013. Figurer 2 shows the 30-min average mixing ratios of these species from 3 to 30 November. There is a large variability in the levels of these gases during this month. Relatively lower values of these gases were observed during 16–19 November, while higher levels were observed during 20–27 November. A plume of very high levels of these gases was observed on 21 November. However, CH4 level was highest on 7 November when the other two gases did not show any abnormal increase. The minimum level of CO2 observed during this month was about 390 ppmv and it went as high as 558.6 ppmv. The average level of CO2 during this month was found to be 418.3 17.6 ppmv. Similarly, minimum level of CO was found to be 0.2 ppmv and highest level (7.0 ppmv) was observed coinciding with the high level of CO2. The average level of CO was found to be 0.73 0.65 ppmv. Methane showed 1.95, 5.7 and 2.41 0.42 ppmv respec-tively, for minimum, highest and mean levels. Maximum variability has been observed in the levels of CO as its lifetime (few weeks to two months) is the shortest among the three gases. To illustrate the nature of average diurnal cycle of CO2, CO and CH4, we standardized it by averaging

Figure 2. Variability in the 30-min average mixing ratios of CO2, CO and CH4 measured at PRL, Ahmedabad during November 2013.



of every second data for respective hour of all the days of observations after removing the outer values beyond two sigma as outliers. Figure 3 shows the average diurnal patterns based on hourly average values for these gases during November 2013. Highest values of hourly average CO2 (431 ppmv) are observed during the morning around 0800 h and in the late evening around 2200 h. These are also the periods of maximum variability. There is a slow increase in the levels of CO2 after the midnight (∼0200 h) low. This is mainly due to respiration by the plants. After the peak around 0800 h, the level of CO2 starts decreasing as the planetary boundary layer (PBL) height increases and as the photosynthesis in plants increases. The minimum CO2 (396 ppmv) during the day is observed around 1600 h, when the diurnal value of air temperature is high-est and the PBL height is also high. This gives higher volume for mixing of the pollutants in the atmosphere. CO2 levels again increase after this minimum due to lowering of the PBL, increase in the vehicular traffic as well as decrease in the photosynthesis activity. The variability in CO2 is high from late night to morning (2–3%) and minimum (0.6%) during afternoon hours. The

Figure 3. Average diurnal variations of CO2, CO and CH4 observed during November 2013 at Ahmedabad.

average diurnal amplitude during this month is about 35 ppmv. The only other measurements for diurnal varia-tions in India are available for Dehradun and Gadanki11, and for Sriharikota10. Dehradun and Gadanki show diur-nal amplitudes of 30 and 40 ppmv respectively, whereas Sriharikota shows an amplitude of only 17 ppmv. Though the diurnal amplitude observed at Ahmedabad is in the range of Dehradun and Gadanki, but their average CO2 levels during 2010–2011 are much lower (∼355–360 ppmv), than the average CO2 level of 418.3 ppmv observed at Ahmedabad during November 2013. CO also shows a similar diurnal variation but with some differ-ences. Its levels keep on decreasing after the evening maximum around 2000 h to a low value of about 0.370 ppmv around 0500 h. After this, there is a slight increase till about 0900 h after which its values start decreasing again. The minimum (0.325 ppmv) is reached around 1500 h. Like CO2, CO values also start increasing after this min-imum, but peak around 2000 h in the evening. Timings are slightly different for CO compared to CO2, which also has the effects of biosphere in addition to fossil-fuel combustion and biomass burning. The variability in CO is also high (30–65%) from late night to early morning, and minimum (16%) during afternoon hours. CH4, on the other hand, has different diurnal variation. After the peak (2.81 ppmv) in the evening around 2200 h, its values keep on decreasing till the afternoon minimum (2.02 ppmv) around 15 h. This species also has high variability (12–16%) in the late night and early morning, and minimum (2%) during afternoon hours. These three gases have some common emission sources and some specific different sources. As men-tioned earlier, CO2 has both biospheric and combustion of fossil fuel and biomass burning. CO has emission sources from fossil-fuel combustion and biomass burning as well as oxidation from hydrocarbons. However, CH4 has dif-ferent sources such as wetlands, rice agriculture, landfills, coal mines, biomass burning as well as losses of com-pressed natural gas (CNG). There are many buses and autos being run on CNG in this city. Hence, the diurnal variations of these gases are different. The only common features are minimum in the afternoon and maximum in late evening/night. These features are dominated by the change in the height of the PBL from highest in the after-noon to lowest in the night/early morning. Also, both CO and CH4 have losses by OH radicals. Since mixing ratios of OH are maximum during noon hours, so is the loss of both these gases. However, the reaction rate of OH with CH4 is very slow (6.3 10–15 cm3 mol–1 s–1), which is only 1/24th that of reaction with CO (1.5 10–13 cm3 mol–1 s–1). The lifetime of CH4 is about 8–10 years. CO2 does not have any chemical loss. Hence its lifetime is highest (∼40–50 years) among these gases. Meteorological factors also play an important role in the distribution of trace gases. We have already discussed



Figure 4. a, Variations of measured CO2, CO and CH4 with wind speed observed at Ahmedabad during November 2013. b, Polar diagrams showing variations of observed trace gases with wind speed and wind direction.

Figure 5. Correlations and slopes for CO–CO2, CH4–CO2 and CO–CH4 combinations during day and night. These are based on 15 min average data observed during November 2013 at Ahmedabad.

the role of PBL earlier. Air temperature and humidity also have an impact on some of the emission sources as well as chemistry. However, advection can change their concentrations significantly. Figure 4 a shows variation of mixing ratios of these species with wind speed. It is clearly

evident that higher wind speed lowers the levels of these gases. However, the correlations are not good as wind di-rection and other factors also contribute to their changes. Figure 4 b shows the wind polar diagrams, colour coded with the mixing ratios of CO2, CO and CH4, for November



2013. The episodes of higher mixing ratios of CO2 (>425 ppmv), CO (>1 ppmv) and CH4 (>2.5 ppmv) are mostly found when wind direction is in between 30 and 150. The main city, a power house and several industries lie in this direction range. Also, higher levels of these gases are observed when the wind speed is low. These three gases have some common emission sources and losses. Correlations and slopes can give some hint on the dominant emission sources or losses. For example, fossil fuel and biomass burning both emit CO2 and CO, but CO emission is more during biomass/biofuel burning compared to that from fossil-fuel combustion. Hence the correlation slopes of CO with CO2 will vary with the effi-ciency of combustion20–22 and thus will provide a good proxy for evaluating the signature of source type and their combustion efficiencies. A good correlation between two gases will indicate if major emission sources or loss processes are common. In order to study the important common sources of CO2, CO and CH4, we calculated the correlations of CO–CO2 and CH4–CO2 separately for day (07 : 00–18 : 45 h) and night (19 : 00–06 : 45 h). The ordi-nary least square regression method has been imposed to the 15-min average data of CO, CH4 and CO2 for estimating the correlation slopes and regression coefficients. Figure 5 shows correlations and slopes for different combinations. Correlation of CO2 with CO is not good, particularly during daytime as CO2 has other biospheric sources and sinks and the lifetimes of the two gases are different. The slope (CO/CO2) is found to be 8 and 37 ppbv/ppmv during day and night respectively, for this month at Ahmedabad. The average slope based on all the data is 16 ppbv/ppmv. The average ratio (CO/CO2) is found around 13.4 ppbv/ppmv over Puducherry, India during April–May–June only based on weekly flask measure-ments during 2007–2011. Similar measurements for Hanle, India, a free tropospheric site, give this ratio in the range 35–55 ppbv/ppmv (ref. 12). Over China, the CO/CO2 ratios are found in the range of 40–45 ppbv/ppmv (refs 23, 24), while southern California shows this ratio to be around 11 ppbv/ppmv (ref. 25). This shows the dominance of fossil fuel at Ahmedabad. The correlation between CH4 and CO2 is found to be higher, 0.67 and 0.55 during day and night respectively, even though they are not emitted from common major sources. The atmospheric lifetimes of both gases are much longer than the timescale for mesoscale transport. Even if emitted from different sources, the dynamics in the boundary layer will cause mixing on very short time-scales. The slope (CH4/CO2) is observed to be 11.1 and 23.8 ppbv/ppmv during day and night respectively, and the slope based on all the data is found to be 18 ppbv/ppmv in November at Ahmedabad. Puducherry, a southeast coastal station shows this ratio in the range of 6–9 ppbv/ppmv (ref. 12). Several studies have reported strong correlation between CH4 and CO2 over different urban regions and observed slopes in the range of 5–8

ppbv/ppmv (refs 25, 26). Higher ratio at Ahmedabad indicates relatively stronger source for methane. Further, we have also derived the slope for CO–CH4 (CO/CH4), which is observed to be 0.71 and 0.95 ppmv/ppmv during day and night respectively. These are not very different from the average slope of 0.96 ppmv/ppmv based on all the data. However, correla-tions are poor (~0.35). Puducherry and Port-Blair show this ratio in the range of 0.4–1.4 ppbv/ppmv (ref. 12). The difference in the slopes is due to different types of emission sources. Continuous measurements of CO2 together with CO and CH4 have been initiated at Ahmedabad. The average values of CO2, CO and CH4 for November 2013 are 418.3 17.6, 0.73 0.65 and 2.41 0.42 ppmv respectively. Higher variabilities have been observed during the day and from day-to-day. Higher variability in CO is due to its relatively shorter lifetime. The observed CO/CO2 slope indicates emissions in Ahmedabad are dominated by fossil fuel. The CH4/CO2 slope is higher than other sites, which suggests that there is significant source for CH4 also in this city. This analysis shows the importance of such simultane-ous measurements to find the major emission sources.

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ACKNOWLEDGEMENTS. We thank PRL, Ahmedabad for encour-agement and ISRO-GBP for financial support. We also thank P. K. Patra (JAMSTEC, Japan) for useful discussions; T. K. Sunilkumar (PRL) for help and S.L. thanks Tom Ryerson (NOAA, Boulder, USA) for useful suggestions about CO2 measurements. We also thank the reviewer for useful comments and suggestions. Received 2 June 2015; revised accepted 5 August 2015 doi: 10.18520/v109/i11/2111-2116

Observations of snow–meteorological parameters in Gangotri glacier region H. S. Gusain*, Manish Kala, Ashwagosha Ganju, V. D. Mishra and Snehmani Snow and Avalanche Study Establishment, Research and Development Centre, Chandigarh 160 036, India In this communication analysis of the snow–meteoro-logical parameters recorded in the Gangotri glacier region has been presented. Maximum temperature, minimum temperature, snowfall, snow cover thick-ness, incoming shortwave radiation flux, reflected shortwave radiation flux and albedo have been recor-ded at ‘Bhojbasa’ observation station. Meteorological data of 13 years from 2000 to 2012 have been pre-sented for annual and seasonal variations in tempera-ture, snowfall and snow cover thickness. Winter, pre-monsoon, monsoon and post-monsoon data have been considered for analysis. Annual mean maximum and minimum temperature are 11.1 0.7C and –2.3 0.4C respectively. Mean values of these parameters obtained for winter season are 3.0 1.0C and –10.4 1.3C respectively. Mean annual snowfall amount is 257.5 81.6 cm and maximum snow cover thickness varies from 42 to 205 cm for different years. Incoming shortwave radiation flux and reflected shortwave radiation flux have been recorded using pyranometer sensor mounted on automatic weather station, and data for 2012 and 2013 are presented. In-coming shortwave radiation flux and total atmos-pheric transmissivity have been estimated. Mean annual atmospheric transmissivity is 0.37 at the ob-servation location. Mean seasonal albedo for winter season is observed to be quite high compared to other seasons. Maximum and minimum temperature reveal an increase of 0.9C and 0.05C respectively, during the decade. Annual snowfall amount reveals a de-crease of 37 cm in the decade. The observed tempera-ture and snowfall patterns during the past 13 years, at the present study location, indicate that trends in Cen-tral Himalaya may be in accordance with the observed trends in the Western Himalaya. Keywords: Albedo, glacier, snowfall, snow cover, temperature. THE Himalaya Mountains are the abode of the largest number of glaciers outside the polar regions. These glaciers are the major source of many perennial river systems, in-cluding the Ganga, Indus and Brahmaputra1. These rivers play a critical role in meeting the demands of water, irri-gation and hydropower1,2. Gangotri glacier is one of the most well-studied glaciers in India and is the largest gla-cier in the Garhwal Himalaya3. Studies of the Gangotri glacier have been conducted for analyses of glacier

tropospheric greenhouse gases: emission characteristics ...library/chandra_n_2015_abst.pdf ·...

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